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Gruson D, Cobbaert C, Dabla PK, Stankovic S, Homsak E, Kotani K, Samir Assaad R, Nichols JH, Gouget B. Validation and verification framework and data integration of biosensors and in vitro diagnostic devices: a position statement of the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MBHLM) and the IFCC Scientific Division. Clin Chem Lab Med 2024; 62:1904-1917. [PMID: 38379410 DOI: 10.1515/cclm-2023-1455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.
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Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium
- Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
| | - Christa Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre (LUMC), Leiden, Netherlands
- International Federation of Clinical Chemistry (IFCC) Scientific Division, Milan, Italy
| | - Pradeep Kumar Dabla
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department of Biochemistry, G.B. Pant Institute of Postgraduate Medical Education & Research, Associated Maulana Azad Medical College, New Delhi, India
| | - Sanja Stankovic
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Center for Medical Biochemistry, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Evgenija Homsak
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department for Laboratory Diagnostics, University Clinical Center Maribor, Maribor, Slovenia
| | - Kazuhiko Kotani
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Division of Community and Family Medicine, Jichi Medical University, Shimotsuke-City, Japan
| | - Ramy Samir Assaad
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Egyptian Association of Healthcare Quality and Patient Safety, Alexandria, Egypt
- Medical Research Institute - Alexandria University, Alexandria, Egypt
| | - James H Nichols
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bernard Gouget
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
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Vahidi M, Rizkalla AS, Mequanint K. Extracellular Matrix-Surrogate Advanced Functional Composite Biomaterials for Tissue Repair and Regeneration. Adv Healthc Mater 2024:e2401218. [PMID: 39036851 DOI: 10.1002/adhm.202401218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/13/2024] [Indexed: 07/23/2024]
Abstract
Native tissues, comprising multiple cell types and extracellular matrix components, are inherently composites. Mimicking the intricate structure, functionality, and dynamic properties of native composite tissues represents a significant frontier in biomaterials science and tissue engineering research. Biomimetic composite biomaterials combine the benefits of different components, such as polymers, ceramics, metals, and biomolecules, to create tissue-template materials that closely simulate the structure and functionality of native tissues. While the design of composite biomaterials and their in vitro testing are frequently reviewed, there is a considerable gap in whole animal studies that provides insight into the progress toward clinical translation. Herein, we provide an insightful critical review of advanced composite biomaterials applicable in several tissues. The incorporation of bioactive cues and signaling molecules into composite biomaterials to mimic the native microenvironment is discussed. Strategies for the spatiotemporal release of growth factors, cytokines, and extracellular matrix proteins are elucidated, highlighting their role in guiding cellular behavior, promoting tissue regeneration, and modulating immune responses. Advanced composite biomaterials design challenges, such as achieving optimal mechanical properties, improving long-term stability, and integrating multifunctionality into composite biomaterials and future directions, are discussed. We believe that this manuscript provides the reader with a timely perspective on composite biomaterials.
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Affiliation(s)
- Milad Vahidi
- Department of Chemical and Biochemical Engineering, The University of Western Ontario, London, N6A5B9, Canada
| | - Amin S Rizkalla
- Department of Chemical and Biochemical Engineering, The University of Western Ontario, London, N6A5B9, Canada
- School of Biomedical Engineering, The University of Western Ontario, London, N6A5B9, Canada
| | - Kibret Mequanint
- Department of Chemical and Biochemical Engineering, The University of Western Ontario, London, N6A5B9, Canada
- School of Biomedical Engineering, The University of Western Ontario, London, N6A5B9, Canada
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Wang N, Zhang H, Qiu X, Gerhard R, van Turnhout J, Cressotti J, Zhao D, Tang L, Cao Y. Recent Advances in Ferroelectret Fabrication, Performance Optimization, and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2400657. [PMID: 38719210 DOI: 10.1002/adma.202400657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 04/24/2024] [Indexed: 05/29/2024]
Abstract
The growing demand for wearable devices has sparked a significant interest in ferroelectret films. They possess flexibility and exceptional piezoelectric properties due to strong macroscopic dipoles formed by charges trapped at the interface of their internal cavities. This review of ferroelectrets focuses on the latest progress in fabrication techniques for high temperature resistant ferroelectrets with regular and engineered cavities, strategies for optimizing their piezoelectric performance, and novel applications. The charging mechanisms of bipolar and unipolar ferroelectrets with closed and open-cavity structures are explained first. Next, the preparation and piezoelectric behavior of ferroelectret films with closed, open, and regular cavity structures using various materials are discussed. Three widely used models for predicting the piezoelectric coefficients (d33) are outlined. Methods for enhancing the piezoelectric performance such as optimized cavity design, utilization of fabric electrodes, injection of additional ions, application of DC bias voltage, and synergy of foam structure and ferroelectric effect are illustrated. A variety of applications of ferroelectret films in acoustic devices, wearable monitors, pressure sensors, and energy harvesters are presented. Finally, the future development trends of ferroelectrets toward fabrication and performance optimization are summarized along with its potential for integration with intelligent systems and large-scale preparation.
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Affiliation(s)
- Ningzhen Wang
- School of Technology, Beijing Forestry University, Beijing, 100083, China
| | - He Zhang
- School of Technology, Beijing Forestry University, Beijing, 100083, China
| | - Xunlin Qiu
- Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Reimund Gerhard
- Institute of Physics and Astronomy, Faculty of Science, University of Potsdam, 14476, Potsdam-Golm, Germany
| | - Jan van Turnhout
- Department of Materials Science and Engineering, Delft University of Technology, Delft, 2628 CD, The Netherlands
| | - Jason Cressotti
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
| | - Dong Zhao
- School of Technology, Beijing Forestry University, Beijing, 100083, China
| | - Liang Tang
- School of Technology, Beijing Forestry University, Beijing, 100083, China
| | - Yang Cao
- Electrical Insulation Research Center, Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA
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Maloney MD, Haddas R, Schwarz EM, Nelms S, Rizzone K. Efforts to Improve Diversity, Equality, and Inclusion in Sports Medicine via Community Engagement Initiatives Within American Cities Divided by Racial, Social, and Economic Factors. Clin Sports Med 2024; 43:271-277. [PMID: 38383109 DOI: 10.1016/j.csm.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Although the twenty-first century has seen major advances in evidence-based medicine to improve health, athletic performance, and injury prevention, our inability to implement these best practices across underserved American communities has limited the impact of these breakthroughs in sports medicine. Rochester, NY is stereotypical of American communities in which an economically challenged racially diverse urban center with grossly underperforming public schools is surrounded by adequately resourced predominantly Caucasian state-of-the-art education systems. As these great disparities perpetuate and further degrade our society in the absence of interventions, the need for community engagement initiatives is self-evident.
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Affiliation(s)
- Michael D Maloney
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA; Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA.
| | - Ram Haddas
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA; Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA
| | - Edward M Schwarz
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA; Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA
| | - Shaun Nelms
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA; Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA
| | - Katherine Rizzone
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY, USA; Department of Orthopedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA
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Melman A, Teng MJ, Coombs DM, Li Q, Billot L, Lung T, Rogan E, Marabani M, Hutchings O, Maher CG, Machado GC. A Virtual Hospital Model of Care for Low Back Pain, Back@Home: Protocol for a Hybrid Effectiveness-Implementation Type-I Study. JMIR Res Protoc 2024; 13:e50146. [PMID: 38386370 PMCID: PMC10921332 DOI: 10.2196/50146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Low back pain (LBP) was the fifth most common reason for an emergency department (ED) visit in 2020-2021 in Australia, with >145,000 presentations. A total of one-third of these patients were subsequently admitted to the hospital. The admitted patient care accounts for half of the total health care expenditure on LBP in Australia. OBJECTIVE The primary aim of the Back@Home study is to assess the effectiveness of a virtual hospital model of care to reduce the length of admission in people presenting to ED with musculoskeletal LBP. A secondary aim is to evaluate the acceptability and feasibility of the virtual hospital and our implementation strategy. We will also investigate rates of traditional hospital admission from the ED, representations and readmissions to the traditional hospital, demonstrate noninferiority of patient-reported outcomes, and assess cost-effectiveness of the new model. METHODS This is a hybrid effectiveness-implementation type-I study. To evaluate effectiveness, we plan to conduct an interrupted time-series study at 3 metropolitan hospitals in Sydney, New South Wales, Australia. Eligible patients will include those aged 16 years or older with a primary diagnosis of musculoskeletal LBP presenting to the ED. The implementation strategy includes clinician education using multimedia resources, staff champions, and an "audit and feedback" process. The implementation of "Back@Home" will be evaluated over 12 months and compared to a 48-month preimplementation period using monthly time-series trends in the average length of hospital stay as the primary outcome. We will construct a plot of the observed and expected lines of trend based on the preimplementation period. Linear segmented regression will identify changes in the level and slope of fitted lines, indicating immediate effects of the intervention, as well as effects over time. The data will be fully anonymized, with informed consent collected for patient-reported outcomes. RESULTS As of December 6, 2023, a total of 108 patients have been cared for through Back@Home. A total of 6 patients have completed semistructured interviews regarding their experience of virtual hospital care for nonserious back pain. All outcomes will be evaluated at 6 months (August 2023) and 12 months post implementation (February 2024). CONCLUSIONS This study will serve to inform ongoing care delivery and implementation strategies of a novel model of care. If found to be effective, it may be adopted by other health districts, adapting the model to their unique local contexts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/50146.
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Affiliation(s)
- Alla Melman
- Sydney Musculoskeletal Health, The University of Sydney and Sydney Local Health District, Camperdown, Australia
| | - Min Jiat Teng
- Sydney Musculoskeletal Health, The University of Sydney and Sydney Local Health District, Camperdown, Australia
- RPA Virtual Hospital, Sydney Local Health District, Sydney, Australia
| | - Danielle M Coombs
- Sydney Musculoskeletal Health, The University of Sydney and Sydney Local Health District, Camperdown, Australia
| | - Qiang Li
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Laurent Billot
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Thomas Lung
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Eileen Rogan
- Department of Medicine, Canterbury Hospital, Sydney Local Health District, Sydney, Australia
| | - Mona Marabani
- Department of Medicine, Canterbury Hospital, Sydney Local Health District, Sydney, Australia
| | - Owen Hutchings
- RPA Virtual Hospital, Sydney Local Health District, Sydney, Australia
| | - Chris G Maher
- Sydney Musculoskeletal Health, The University of Sydney and Sydney Local Health District, Camperdown, Australia
| | - Gustavo C Machado
- Sydney Musculoskeletal Health, The University of Sydney and Sydney Local Health District, Camperdown, Australia
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Garcia-Moreno FM, Bermudez-Edo M, Pérez-Mármol JM, Garrido JL, Rodríguez-Fórtiz MJ. Systematic design of health monitoring systems centered on older adults and ADLs. BMC Med Inform Decis Mak 2024; 23:300. [PMID: 38350979 PMCID: PMC10863048 DOI: 10.1186/s12911-024-02432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 01/19/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. METHOD A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. RESULTS The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. CONCLUSIONS The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.
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Affiliation(s)
- Francisco M Garcia-Moreno
- Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain.
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain.
| | - Maria Bermudez-Edo
- Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
| | - José Manuel Pérez-Mármol
- Department of Physiotherapy, Faculty of Health Sciences, University of Granada, Av. de la Ilustración, 60, 18016, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Jose Luis Garrido
- Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
| | - María José Rodríguez-Fórtiz
- Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
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Zillner L, Andreas M, Mach M. Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery-a systematic review. Mhealth 2023; 10:8. [PMID: 38323143 PMCID: PMC10839520 DOI: 10.21037/mhealth-23-19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/24/2023] [Indexed: 02/08/2024] Open
Abstract
Background This systematic review aims to highlight the untapped potential of heart rate variability (HRV) and atrial fibrillation (AF) monitoring by wearable health monitoring devices as a critical diagnostic tool in cardiac surgery (CS) patients. We reviewed established predictive capabilities of HRV and AF monitoring in specific cardiosurgical scenarios and provide a perspective on additional predictive properties of wearable health monitoring devices that need to be investigated. Methods After screening most relevant databases, we included 33 publications in this review. Perusing these publications on HRV's prognostic value, we could identify HRV as a predictor for sudden cardiac death, mortality after acute myocardial infarction (AMI), and post operative atrial fibrillation (POAF). With regards to standard AF assessment, which typically includes extensive periods of unrecorded cardiac activity, we demonstrated that continuous monitoring via wearables recorded significant cardiac events that would otherwise have been missed. Results Photoplethysmography and single-lead electrocardiogram (ECG) were identified as the most useful and convenient technical assessment modalities, and their advantages and disadvantages were described in detail. As a call to further action, we observed that the scientific community has relatively extensively explored wearable AF screening, whereas HRV assessment to improve relevant clinical outcomes in CS is rarely studied; it still has great potential to be leveraged. Conclusions Therefore, risk assessment in CS would benefit greatly from earlier preoperative and postoperative AF detection, comprehensive and accurate assessment of cardiac health through HRV metrics, and continuous long-term monitoring. These should be achievable via commercially available wearables.
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Affiliation(s)
- Liliane Zillner
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
| | - Martin Andreas
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
| | - Markus Mach
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
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Lou Z, Tao J, Wei B, Jiang X, Cheng S, Wang Z, Qin C, Liang R, Guo H, Zhu L, Müller‐Buschbaum P, Cheng H, Xu X. Near-Infrared Organic Photodetectors toward Skin-Integrated Photoplethysmography-Electrocardiography Multimodal Sensing System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304174. [PMID: 37991135 PMCID: PMC10754100 DOI: 10.1002/advs.202304174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/05/2023] [Indexed: 11/23/2023]
Abstract
In the fast-evolving landscape of decentralized and personalized healthcare, the need for multimodal biosensing systems that integrate seamlessly with the human body is growing rapidly. This presents a significant challenge in devising ultraflexible configurations that can accommodate multiple sensors and designing high-performance sensing components that remain stable over long periods. To overcome these challenges, ultraflexible organic photodetectors (OPDs) that exhibit exceptional performance under near-infrared illumination while maintaining long-term stability are developed. These ultraflexible OPDs demonstrate a photoresponsivity of 0.53 A W-1 under 940 nm, shot-noise-limited specific detectivity of 3.4 × 1013 Jones, and cut-off response frequency beyond 1 MHz at -3 dB. As a result, the flexible photoplethysmography sensor boasts a high signal-to-noise ratio and stable peak-to-peak amplitude under hypoxic and hypoperfusion conditions, outperforming commercial finger pulse oximeters. This ensures precise extraction of blood oxygen saturation in dynamic working conditions. Ultraflexible OPDs are further integrated with conductive polymer electrodes on an ultrathin hydrogel substrate, allowing for direct interface with soft and dynamic skin. This skin-integrated sensing platform provides accurate measurement of photoelectric and biopotential signals in a time-synchronized manner, reproducing the functionality of conventional technologies without their inherent limitations.
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Affiliation(s)
- Zirui Lou
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
- School of Advanced MaterialsPeking University Shenzhen Graduate SchoolShenzhen518055China
| | - Jun Tao
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
| | - Binbin Wei
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
| | - Xinyu Jiang
- Lehrstuhl für Funktionelle MaterialienPhysik DepartmentTechnische Universität MünchenJames‐Franck‐Str. 185748GarchingGermany
| | - Simin Cheng
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
| | - Zehao Wang
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
| | - Chao Qin
- State Key Laboratory of Silicon and Advanced Semiconductor MaterialsSchool of Materials Science and EngineeringZhejiang UniversityHangzhou310027China
| | - Rong Liang
- State Key Laboratory of Silicon and Advanced Semiconductor MaterialsSchool of Materials Science and EngineeringZhejiang UniversityHangzhou310027China
| | - Haotian Guo
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
| | - Liping Zhu
- State Key Laboratory of Silicon and Advanced Semiconductor MaterialsSchool of Materials Science and EngineeringZhejiang UniversityHangzhou310027China
| | - Peter Müller‐Buschbaum
- Lehrstuhl für Funktionelle MaterialienPhysik DepartmentTechnische Universität MünchenJames‐Franck‐Str. 185748GarchingGermany
- Heinz Maier‐Leibnitz‐Zentrum (MLZ)Technische Universität MünchenLichtenbergstr. 185748GarchingGermany
| | - Hui‐Ming Cheng
- Institute of Technology for Carbon Neutrality & Faculty of Materials Science and Energy EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China
- Shenyang National Laboratory for Materials ScienceInstitute of Metal ResearchChinese Academy of SciencesShenyang110016China
| | - Xiaomin Xu
- Shenzhen International Graduate School & Tsinghua‐Berkeley Shenzhen InstituteTsinghua UniversityShenzhen518055China
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Turcu AM, Ilie AC, Ștefăniu R, Țăranu SM, Sandu IA, Alexa-Stratulat T, Pîslaru AI, Alexa ID. The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events. Diagnostics (Basel) 2023; 13:2382. [PMID: 37510126 PMCID: PMC10378206 DOI: 10.3390/diagnostics13142382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO2), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.
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Affiliation(s)
- Ana-Maria Turcu
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Adina Carmen Ilie
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ramona Ștefăniu
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Sabinne Marie Țăranu
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ioana Alexandra Sandu
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Teodora Alexa-Stratulat
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Anca Iuliana Pîslaru
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ioana Dana Alexa
- Department of Medical Specialties II, Grigore T Popa University of Medicine and Pharmacy, 700115 Iasi, Romania
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Haddas R, Lawlor M, Moghadam E, Fields A, Wood A. Spine patient care with wearable medical technology: state-of-the-art, opportunities, and challenges: a systematic review. Spine J 2023; 23:929-944. [PMID: 36893918 DOI: 10.1016/j.spinee.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/11/2023]
Abstract
BACKGROUND CONTEXT Healthcare reforms that demand quantitative outcomes and technical innovations have emphasized the use of Disability and Functional Outcome Measurements (DFOMs) to spinal conditions and interventions. Virtual healthcare has become increasingly important following the COVID-19 pandemic and wearable medical devices have proven to be a useful adjunct. Thus, given the advancement of wearable technology, broad adoption of commercial devices (ie, smartwatches, phone applications, and wearable monitors) by the general public, and the growing demand from consumers to take control of their health, the medical industry is now primed to formally incorporate evidence-based wearable device-mediated telehealth into standards of care. PURPOSE To (1) identify all wearable devices in the peer-reviewed literature that were used to assess DFOMs in Spine, (2) analyze clinical studies implementing such devices in spine care, and (3) provide clinical commentary on how such devices might be integrated into standards of care. STUDY DESIGN/SETTING A systematic review. METHODS A comprehensive systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) across the following databases: PubMed; MEDLINE; EMBASE (Elsevier); and Scopus. Articles related to wearables systems in spine healthcare were selected. Extracted data was collected as per a predetermined checklist including wearable device type, study design, and clinical indices studied. RESULTS Of the 2,646 publications that were initially screened, 55 were extensively analyzed and selected for retrieval. Ultimately 39 publications were identified as being suitable for inclusion based on the relevance of their content to the core objectives of this systematic review. The most relevant studies were included, with a focus on wearables technologies that can be used in patients' home environments. CONCLUSIONS Wearable technologies mentioned in this paper have the potential to revolutionize spine healthcare through their ability to collect data continuously and in any environment. In this paper, the vast majority of wearable spine devices rely exclusively on accelerometers. Thus, these metrics provide information about general health rather than specific impairments caused by spinal conditions. As wearable technology becomes more prevalent in orthopedics, healthcare costs may be reduced and patient outcomes will improve. A combination of DFOMs gathered using a wearable device in conjunction with patient-reported outcomes and radiographic measurements will provide a comprehensive evaluation of a spine patient's health and assist the physician with patient-specific treatment decision-making. Establishing these ubiquitous diagnostic capabilities will allow improvement in patient monitoring and help us learn about postoperative recovery and the impact of our interventions.
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Affiliation(s)
- Ram Haddas
- University of Rochester Medical Center, Rochester, NY 14642, USA.
| | - Mark Lawlor
- University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Ehsan Moghadam
- University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Andrew Fields
- Medtronic Spine & Biologics, University of Rochester Medical Center, Rochester, NY 14642, USA
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11
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Iqbal U, Imtiaz R, Saudagar AKJ, Alam KA. CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images. Diagnostics (Basel) 2023; 13:diagnostics13101783. [PMID: 37238266 DOI: 10.3390/diagnostics13101783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/01/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body's internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).
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Affiliation(s)
- Uzair Iqbal
- Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad 44000, Pakistan
| | - Romil Imtiaz
- Information and Communication Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Khubaib Amjad Alam
- Department of Software Engineering, National University of Computer and Emerging Sciences, Islamabad Campus, Islamabad 44000, Pakistan
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12
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Rodríguez-Rodríguez I, Campo-Valera M, Rodríguez JV, Frisa-Rubio A. Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073665. [PMID: 37050725 PMCID: PMC10099355 DOI: 10.3390/s23073665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/21/2023] [Accepted: 03/28/2023] [Indexed: 06/12/2023]
Abstract
Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.
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Affiliation(s)
| | - María Campo-Valera
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - José-Víctor Rodríguez
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Alberto Frisa-Rubio
- CIRCE—Centro Tecnológico (Research Centre for Energy Resources and Consumption), Av. Ranillas, Edf. Dinamiza 3D, 50018 Zaragoza, Spain
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13
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Callihan M, Cole H, Stokley H, Gunter J, Clamp K, Martin A, Doherty H. Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020877. [PMID: 36679676 PMCID: PMC9865127 DOI: 10.3390/s23020877] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system. METHODS Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy. RESULTS High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance. CONCLUSIONS The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.
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Zhu Q, Wu T, Wang N. From Piezoelectric Nanogenerator to Non-Invasive Medical Sensor: A Review. BIOSENSORS 2023; 13:113. [PMID: 36671948 PMCID: PMC9856170 DOI: 10.3390/bios13010113] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/01/2023] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Piezoelectric nanogenerators (PENGs) not only are able to harvest mechanical energy from the ambient environment or body and convert mechanical signals into electricity but can also inform us about pathophysiological changes and communicate this information using electrical signals, thus acting as medical sensors to provide personalized medical solutions to patients. In this review, we aim to present the latest advances in PENG-based non-invasive sensors for clinical diagnosis and medical treatment. While we begin with the basic principles of PENGs and their applications in energy harvesting, this review focuses on the medical sensing applications of PENGs, including detection mechanisms, material selection, and adaptive design, which are oriented toward disease diagnosis. Considering the non-invasive in vitro application scenario, discussions about the individualized designs that are intended to balance a high performance, durability, comfortability, and skin-friendliness are mainly divided into two types: mechanical sensors and biosensors, according to the key role of piezoelectric effects in disease diagnosis. The shortcomings, challenges, and possible corresponding solutions of PENG-based medical sensing devices are also highlighted, promoting the development of robust, reliable, scalable, and cost-effective medical systems that are helpful for the public.
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Affiliation(s)
- Qiliang Zhu
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Tong Wu
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
- National Institute of Metrology, Beijing 100029, China
| | - Ning Wang
- Center for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
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15
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Peimankar A, Winther TS, Ebrahimi A, Wiil UK. A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders. SENSORS (BASEL, SWITZERLAND) 2023; 23:679. [PMID: 36679471 PMCID: PMC9866459 DOI: 10.3390/s23020679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/25/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.
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Li W, Chen J, Lan F. Human thermal sensation algorithm modelization via physiological thermoregulatory responses based on dynamic thermal environment tests on males. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107198. [PMID: 36323178 DOI: 10.1016/j.cmpb.2022.107198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/16/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Thermal conditions are changeable in cabin space, where occupants could suffer consecutive self-thermoregulation to such changing thermal stresses. Thermal environment management is expected to be purposefully auto-adjustable for the environment by recognizing individual real-time thermal sensations. Current thermal sensation evaluation models are developed for virtual simulations rather than for realistic scenarios, challenging to evaluate human thermal sensation in the field surveys. METHODS The study constructs a human thermal sensation model via human physiological responses to evaluate the human thermal sensation in the actual vehicle environment. The thermal sensation model forms with exponential functions to clarify the relationship between thermal sensation and pulse rate and blood pressure, which successfully expresses the approximately linear trend around neutral sensation and compensates for the end-points bias. The study set up experimental cases to determine the parameter states in the thermal sensation model. Firstly, subjective thermal sensation scoring was performed by combing with an established seven-point-scale questionnaire survey system for human thermal sensation. Wearable sensors are then applied to measure the human physiological response, including blood pressure BP, pulse rate PR and blood oxygen saturation SpO2. RESULTS The subjects revealed significantly higher pulse rates (positively correlated) and lower blood pressure (negatively correlated) in the warm chamber than in the cool chamber. The defined parameter change rate effectively reveals the trend of human thermal sensation and avoids the inconsistency of raw physiological response levels. The change rate in PR and MAP between the thermal sensation in cold -3 and hot +3 is about a 10% difference. CONCLUSIONS Based on the thermal sensation model algorithm, model parameters were fitted by the subjects' thermal sensation voting and the change rate of their physiological responses. With the coefficient of determination (R2) of the regression over 0.8, the proposed thermal sensation model can be employed for human thermal sensation evaluation. The physiological thermoregulatory responses effectively indicate the thermal state of the human body and can be used in thermal environments in conjunction with human smart wearable devices.
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Affiliation(s)
- Weijian Li
- School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China; Guangdong Key Laboratory of Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China
| | - Jiqing Chen
- School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China; Guangdong Key Laboratory of Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China
| | - Fengchong Lan
- School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China; Guangdong Key Laboratory of Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China.
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Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
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Affiliation(s)
- Aamir Javaid
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Fawzi Zghyer
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Chang Kim
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Erin M. Spaulding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Nino Isakadze
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Jie Ding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Daniel Kargillis
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Yumin Gao
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Faisal Rahman
- Division of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Donald E. Brown
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Suchi Saria
- Machine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA
| | - Seth S. Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Christopher M. Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Francoise A. Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
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Lee P, Kim H, Zitouni MS, Khandoker A, Jelinek HF, Hadjileontiadis L, Lee U, Jeong Y. Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e40797. [PMID: 36378505 PMCID: PMC9709670 DOI: 10.2196/40797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/30/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. OBJECTIVE This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? METHODS We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. RESULTS Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. CONCLUSIONS A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.
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Affiliation(s)
- Peter Lee
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Heepyung Kim
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - M Sami Zitouni
- College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates
| | - Ahsan Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Uichin Lee
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Yong Jeong
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Garikapati K, Turnbull S, Bennett RG, Campbell TG, Kanawati J, Wong MS, Thomas SP, Chow CK, Kumar S. The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias. Heart Lung Circ 2022; 31:1432-1449. [PMID: 36109292 DOI: 10.1016/j.hlc.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/18/2022] [Accepted: 08/01/2022] [Indexed: 10/14/2022]
Abstract
Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.
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Affiliation(s)
- Kartheek Garikapati
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Samual Turnbull
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Richard G Bennett
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Timothy G Campbell
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Juliana Kanawati
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Mary S Wong
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Stuart P Thomas
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Clara K Chow
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Saurabh Kumar
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia.
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Wearables in Cardiovascular Disease. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10314-0. [PMID: 36085432 DOI: 10.1007/s12265-022-10314-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/29/2022] [Indexed: 10/14/2022]
Abstract
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.
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Cheong SHR, Ng YJX, Lau Y, Lau ST. Wearable technology for early detection of COVID-19: A systematic scoping review. Prev Med 2022; 162:107170. [PMID: 35878707 PMCID: PMC9304072 DOI: 10.1016/j.ypmed.2022.107170] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/29/2022] [Accepted: 07/17/2022] [Indexed: 11/23/2022]
Abstract
Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
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Affiliation(s)
- Shing Hui Reina Cheong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Yu Jie Xavia Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Siew Tiang Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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22
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Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:4890. [PMID: 35808386 PMCID: PMC9269150 DOI: 10.3390/s22134890] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.
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Affiliation(s)
- Serena Zanelli
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
| | - Mehdi Ammi
- University of Paris 8, LAGA, CNRS, Institut Galilée, 93200 Saint Denis, France;
| | | | - Mounim A. El Yacoubi
- SAMOVAR Telecom SudParis, CNRS, Institut Polytechnique de Paris, 91764 Paris, France;
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23
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Lee P, Kim H, Kim Y, Choi W, Zitouni MS, Khandoker A, Jelinek HF, Hadjileontiadis L, Lee U, Jeong Y. Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management. JMIR Mhealth Uhealth 2022; 10:e38614. [PMID: 35679029 PMCID: PMC9217147 DOI: 10.2196/38614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/20/2022] [Accepted: 06/08/2022] [Indexed: 12/15/2022] Open
Abstract
Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various "smart" mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.
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Affiliation(s)
- Peter Lee
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Heepyung Kim
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Yongshin Kim
- Graduate School of Data Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Woohyeok Choi
- Information & Electronics Research Institute, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - M Sami Zitouni
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Ahsan Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Uichin Lee
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Yong Jeong
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Guan Y, Bai M, Li Q, Li W, Liu G, Liu C, Chen Y, Lin Y, Hui Y, Wei R. A plantar wearable pressure sensor based on hybrid lead zirconate-titanate/microfibrillated cellulose piezoelectric composite films for human health monitoring. LAB ON A CHIP 2022; 22:2376-2391. [PMID: 35635092 DOI: 10.1039/d2lc00051b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Flexible and wearable electronic sensors hold great promise for improving the quality of life, especially in the field of healthcare monitoring, owing to their low cost, flexibility, high electromechanical coupling performance, high sensitivity, and biocompatibility. To achieve high piezoelectric performance similar to that of rigid materials while satisfying the flexible requirements for wearable sensors, we propose novel hybrid films based on lead zirconate titanate powder and microfibrillated cellulose (PZT/MFC) for plantar pressure measurements. The flexible films made using the polarization process are tested. It was found that the maximum piezoelectric coefficient was 31 pC N-1 and the maximum tensile force of the flexible films was 26 N. A wide range of bending angles between 15° and 180° proves the flexibility capability of the films. In addition, the charge density shows a proportional relation with the applied mechanical force, and it could sense stress of 1 kPa. Finally, plantar pressure sensors are arranged and packaged with a film array followed by connection with the detection module. Then, the pressure curves of each point on the plantar are obtained. Through analysis of the curve, several parameters of human body motions that are important in the rehabilitation of diabetic patients and the detection of sports injury can be performed, including stride frequency, length and speed. Overall, the proposed PZT/MFC wearable plantar pressure sensor has broad application prospects in the field of sports injury detection and medical rehabilitation training.
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Affiliation(s)
- Yanfang Guan
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
- National Engineering Laboratory/Key Laboratory of Henan Province, Henan University of Technology, Zhengzhou 450001, China
| | - Mingyang Bai
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Qiuliang Li
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Wujie Li
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Guangyu Liu
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Chunbo Liu
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Yu Chen
- School of Mechanical Engineering, Chengdu University, Chengdu, 610106, China
| | - Yang Lin
- Department of Mechanical, Industrial & Systems Engineering, University of Rhode Island, Kingston 02881, USA
| | - Yanbo Hui
- School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China.
| | - Ronghan Wei
- Advanced Intelligent Manufacturing, Nano Opto-mechatronics & Biomedical Engineering Lab, Zhengzhou University, Zhengzhou 450001, China
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25
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State of the Art in Smart Portable, Wearable, Ingestible and Implantable Devices for Health Status Monitoring and Disease Management. SENSORS 2022; 22:s22114228. [PMID: 35684847 PMCID: PMC9185336 DOI: 10.3390/s22114228] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 02/01/2023]
Abstract
Several illnesses that are chronic and acute are becoming more relevant as the world's aging population expands, and the medical sector is transforming rapidly, as a consequence of which the need for "point-of-care" (POC), identification/detection, and real time management of health issues that have been required for a long time are increasing. Biomarkers are biological markers that help to detect status of health or disease. Biosensors' applications are for screening for early detection, chronic disease treatment, health management, and well-being surveillance. Smart devices that allow continual monitoring of vital biomarkers for physiological health monitoring, medical diagnosis, and assessment are becoming increasingly widespread in a variety of applications, ranging from biomedical to healthcare systems of surveillance and monitoring. The term "smart" is used due to the ability of these devices to extract data with intelligence and in real time. Wearable, implantable, ingestible, and portable devices can all be considered smart devices; this is due to their ability of smart interpretation of data, through their smart sensors or biosensors and indicators. Wearable and portable devices have progressed more and more in the shape of various accessories, integrated clothes, and body attachments and inserts. Moreover, implantable and ingestible devices allow for the medical diagnosis and treatment of patients using tiny sensors and biomedical gadgets or devices have become available, thus increasing the quality and efficacy of medical treatments by a significant margin. This article summarizes the state of the art in portable, wearable, ingestible, and implantable devices for health status monitoring and disease management and their possible applications. It also identifies some new technologies that have the potential to contribute to the development of personalized care. Further, these devices are non-invasive in nature, providing information with accuracy and in given time, thus making these devices important for the future use of humanity.
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26
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Saleem JJ, Wilck NR, Murphy JJ, Herout J. Veteran and Staff Experience from a Pilot Program of Health Care System-Distributed Wearable Devices and Data Sharing. Appl Clin Inform 2022; 13:532-540. [PMID: 35613912 DOI: 10.1055/s-0042-1748857] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
OBJECTIVE The growing trend to use wearable devices to track activity and health data has the potential to positively impact the patient experience with their health care at home and with their care team. As part of a pilot program, the U.S. Department of Veterans Affairs (VA) distributed Fitbits to Veterans through four VA medical centers. Our objective was to assess the program from both Veterans' and clinicians' viewpoints. Specifically, we aimed to understand barriers to Fitbit setup and use for Veterans, including syncing devices with a VA mobile application (app) to share data, and assess the perceived value of the device functions and ability to share information from the Fitbit with their care team. In addition, we explored the clinicians' perspective, including how they expected to use the patient-generated health data (PGHD). METHODS We performed semi-structured interviews with 26 Veterans and 16 VA clinicians to assess the program. Responses to each question were summarized in order of frequency of occurrence across participants and audited by an independent analyst for accuracy. RESULTS Our findings reveal that despite setup challenges, there is support for the use of Fitbits to engage Veterans and help manage their health. Clinicians believed there were benefits for having Veterans use the Fitbits and expected to use the PGHD in a variety of ways as part of the Veterans' care plans, including monitoring progress toward health behavior goals. Veterans were overwhelmingly enthusiastic about using the Fitbits; this enthusiasm seems to extend beyond the 3 month "novelty period." CONCLUSION The pilot program for distributing Fitbits to Veterans appears to be successful from both Veterans' and clinicians' perspectives and suggests that expanded use of wearable devices should be considered. Future studies will need to carefully consider how to incorporate the PGHD into the electronic health record and clinical workflow.
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Affiliation(s)
- Jason J Saleem
- Department of Industrial Engineering, J.B. Speed School of Engineering, University of Louisville, Louisville, Kentucky, United States.,Center for Human Systems Engineering, University of Louisville, Louisville, Kentucky, United States
| | - Nancy R Wilck
- Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
| | - John J Murphy
- Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
| | - Jennifer Herout
- Department of Veterans Affairs (VA), Office of Connected Care, Patient Care Services, Veterans Health Administration, Washington, District of Columbia, United States
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27
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Lowering the Sampling Rate: Heart Rate Response during Cognitive Fatigue. BIOSENSORS 2022; 12:bios12050315. [PMID: 35624616 PMCID: PMC9139121 DOI: 10.3390/bios12050315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/22/2022]
Abstract
Cognitive fatigue is a mental state characterised by feelings of tiredness and impaired cognitive functioning due to sustained cognitive demands. Frequency-domain heart rate variability (HRV) features have been found to vary as a function of cognitive fatigue. However, it has yet to be determined whether HRV features derived from electrocardiogram data with a low sampling rate would remain sensitive to cognitive fatigue. Bridging this research gap is important as it has substantial implications for designing more energy-efficient and less memory-hungry wearables to monitor cognitive fatigue. This study aimed to examine (1) the level of agreement between frequency-domain HRV features derived from lower and higher sampling rates, and (2) whether frequency-domain HRV features derived from lower sampling rates could predict cognitive fatigue. Participants (N = 53) were put through a cognitively fatiguing 2-back task for 20 min whilst their electrocardiograms were recorded. Results revealed that frequency-domain HRV features derived from sampling rate as low as 125 Hz remained almost perfectly in agreement with features derived from the original sampling rate at 2000 Hz. Furthermore, frequency domain features, such as normalised low-frequency power, normalised high-frequency power, and the ratio of low- to high-frequency power varied as a function of increasing cognitive fatigue during the task across all sampling rates. In conclusion, it appears that sampling at 125 Hz is more than adequate for frequency-domain feature extraction to index cognitive fatigue. These findings have significant implications for the design of low-cost wearables for detecting cognitive fatigue.
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28
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Hossain MB, Kong Y, Posada-Quintero HF, Chon KH. Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact. SENSORS (BASEL, SWITZERLAND) 2022; 22:3177. [PMID: 35590866 PMCID: PMC9104297 DOI: 10.3390/s22093177] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.
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Affiliation(s)
| | | | | | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (M.-B.H.); (Y.K.); (H.F.P.-Q.)
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Wang YC, Xu X, Hajra A, Apple S, Kharawala A, Duarte G, Liaqat W, Fu Y, Li W, Chen Y, Faillace RT. Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics (Basel) 2022; 12:diagnostics12030689. [PMID: 35328243 PMCID: PMC8947563 DOI: 10.3390/diagnostics12030689] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 02/04/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.
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Affiliation(s)
- Yu-Chiang Wang
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
- Correspondence:
| | - Xiaobo Xu
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Adrija Hajra
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Samuel Apple
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Amrin Kharawala
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Gustavo Duarte
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Wasla Liaqat
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiwen Fu
- Department of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USA;
| | - Weijia Li
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiyun Chen
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Robert T. Faillace
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
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Logaras E, Billis A, Kokkinidis I, Ketseridou S, Fourlis A, Imprialos K, Tzotzis A, Doumas M, Bamidis P. Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics with the Use of Real-World Data and Artificial Intelligence: Observational Study (Preprint). JMIR Form Res 2022; 6:e36933. [DOI: 10.2196/36933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
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31
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Huhn S, Axt M, Gunga HC, Maggioni MA, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e34384. [PMID: 35076409 PMCID: PMC8826148 DOI: 10.2196/34384] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/23/2021] [Accepted: 12/17/2021] [Indexed: 12/23/2022] Open
Abstract
Background Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. Objective In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. Methods We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing. Results We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). Conclusions Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.
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Affiliation(s)
- Sophie Huhn
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Miriam Axt
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Hanns-Christian Gunga
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany
| | - Martina Anna Maggioni
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy
| | | | - David Obor
- Kenya Medical Research Institute, Kisumu, Kenya
| | - Ali Sié
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.,Centre de Recherche en Santé Nouna, Nouna, Burkina Faso
| | | | - Aditi Bunker
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Rainer Sauerborn
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Till Bärnighausen
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.,Harvard Center for Population and Development Studies, Cambridge, MA, United States.,Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Sandra Barteit
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
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Snider EJ, Vega SJ, Ross E, Berard D, Hernandez-Torres SI, Salinas J, Boice EN. Supervisory Algorithm for Autonomous Hemodynamic Management Systems. SENSORS (BASEL, SWITZERLAND) 2022; 22:529. [PMID: 35062489 PMCID: PMC8780453 DOI: 10.3390/s22020529] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/07/2022] [Accepted: 01/09/2022] [Indexed: 02/04/2023]
Abstract
Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.
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Affiliation(s)
- Eric J. Snider
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Saul J. Vega
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Evan Ross
- Blood and Shock Resuscitation Group, United States Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - David Berard
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Sofia I. Hernandez-Torres
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Jose Salinas
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Emily N. Boice
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
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Arabshahi M, Wang D, Sun J, Rahnamayiezekavat P, Tang W, Wang Y, Wang X. Review on Sensing Technology Adoption in the Construction Industry. SENSORS 2021; 21:s21248307. [PMID: 34960401 PMCID: PMC8704534 DOI: 10.3390/s21248307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 11/16/2022]
Abstract
Sensing technologies demonstrate promising potential in providing the construction industry with a safe, productive, and high-quality process. The majority of sensing technologies in the construction research area have been focused on construction automation research in prefabrication, on-site operation, and logistics. However, most of these technologies are either not implemented in real construction projects or are at the very early stages in practice. The corresponding applications are far behind, even in extensively researched aspects such as Radio Frequency Identification, ultra-wideband technology, and Fiber Optic Sensing technology. This review systematically investigates the current status of sensing technologies in construction from 187 articles and explores the reasons responsible for their slow adoption from 69 articles. First, this paper identifies common sensing technologies and investigates their implementation extent. Second, contributions and limitations of sensing technologies are elaborated to understand the current status. Third, key factors influencing the adoption of sensing technologies are extracted from construction stakeholders' experience. Demand towards sensing technologies, benefits and suitability of them, and barriers to their adoption are reviewed. Lastly, the governance framework is determined as the research tendency facilitating sensing technologies adoption. This paper provides a theoretical basis for the governance framework development. It will promote the sensing technologies adoption and improve construction performance including safety, productivity, and quality.
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Affiliation(s)
- Mona Arabshahi
- School of Design and Built Environment, Curtin University, Perth, WA 6102, Australia; (M.A.); (Y.W.)
| | - Di Wang
- School of Civil Engineering, Chongqing University, Chongqing 400045, China; (D.W.); (W.T.)
| | - Junbo Sun
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang 213300, China;
| | | | - Weichen Tang
- School of Civil Engineering, Chongqing University, Chongqing 400045, China; (D.W.); (W.T.)
| | - Yufei Wang
- School of Design and Built Environment, Curtin University, Perth, WA 6102, Australia; (M.A.); (Y.W.)
| | - Xiangyu Wang
- School of Design and Built Environment, Curtin University, Perth, WA 6102, Australia; (M.A.); (Y.W.)
- Correspondence:
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Robinson JA, Novak R, Kanduč T, Maggos T, Pardali D, Stamatelopoulou A, Saraga D, Vienneau D, Flückiger B, Mikeš O, Degrendele C, Sáňka O, García Dos Santos-Alves S, Visave J, Gotti A, Persico MG, Chapizanis D, Petridis I, Karakitsios S, Sarigiannis DA, Kocman D. User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12544. [PMID: 34886269 PMCID: PMC8656880 DOI: 10.3390/ijerph182312544] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 01/16/2023]
Abstract
Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human-information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.
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Affiliation(s)
- Johanna Amalia Robinson
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (R.N.); (T.K.); (D.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Rok Novak
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (R.N.); (T.K.); (D.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Tjaša Kanduč
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (R.N.); (T.K.); (D.K.)
| | - Thomas Maggos
- Atmospheric Chemistry and Innovative Technologies Laboratory, NCSR Demokritos, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Demetra Pardali
- Atmospheric Chemistry and Innovative Technologies Laboratory, NCSR Demokritos, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Asimina Stamatelopoulou
- Atmospheric Chemistry and Innovative Technologies Laboratory, NCSR Demokritos, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Dikaia Saraga
- Atmospheric Chemistry and Innovative Technologies Laboratory, NCSR Demokritos, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute (Swiss TPH), CH-4051 Basel, Switzerland; (D.V.); (B.F.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Benjamin Flückiger
- Swiss Tropical and Public Health Institute (Swiss TPH), CH-4051 Basel, Switzerland; (D.V.); (B.F.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Ondřej Mikeš
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Céline Degrendele
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
- Laboratory of Chemistry and Environment, Aix Marseille University, 13003 Marseille, France
| | - Ondřej Sáňka
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Saul García Dos Santos-Alves
- Institute of Health Carlos III (ISCIII), National Environmental Health Centre, Department of Atmospheric Pollution, 28220 Madrid, Spain;
| | - Jaideep Visave
- Department of Science, Technology and Society, University School for Advanced Study IUSS, 27100 Pavia, Italy; (J.V.); (M.G.P.); (D.A.S.)
| | - Alberto Gotti
- EUCENTRE, European Centre for Training and Research in Earthquake Engineering, 27100 Pavia, Italy;
| | - Marco Giovanni Persico
- Department of Science, Technology and Society, University School for Advanced Study IUSS, 27100 Pavia, Italy; (J.V.); (M.G.P.); (D.A.S.)
- EUCENTRE, European Centre for Training and Research in Earthquake Engineering, 27100 Pavia, Italy;
| | - Dimitris Chapizanis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (I.P.); (S.K.)
| | - Ioannis Petridis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (I.P.); (S.K.)
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (I.P.); (S.K.)
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 57001 Thessaloniki, Greece
| | - Dimosthenis A. Sarigiannis
- Department of Science, Technology and Society, University School for Advanced Study IUSS, 27100 Pavia, Italy; (J.V.); (M.G.P.); (D.A.S.)
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (I.P.); (S.K.)
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 57001 Thessaloniki, Greece
| | - David Kocman
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (R.N.); (T.K.); (D.K.)
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Kim J, Lee Y, Kang M, Hu L, Zhao S, Ahn JH. 2D Materials for Skin-Mountable Electronic Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2005858. [PMID: 33998064 DOI: 10.1002/adma.202005858] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Indexed: 06/12/2023]
Abstract
Skin-mountable devices that can directly measure various biosignals and external stimuli and communicate the information to the users have been actively studied owing to increasing demand for wearable electronics and newer healthcare systems. Research on skin-mountable devices is mainly focused on those materials and mechanical design aspects that satisfy the device fabrication requirements on unusual substrates like skin and also for achieving good sensing capabilities and stable device operation in high-strain conditions. 2D materials that are atomically thin and possess unique electrical and optical properties offer several important features that can address the challenging needs in wearable, skin-mountable electronic devices. Herein, recent research progress on skin-mountable devices based on 2D materials that exhibit a variety of device functions including information input and output and in vitro and in vivo healthcare and diagnosis is reviewed. The challenges, potential solutions, and perspectives on trends for future work are also discussed.
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Affiliation(s)
- Jejung Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Yongjun Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Minpyo Kang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Luhing Hu
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Songfang Zhao
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- School of Material Science and Engineering, University of Jinan, Jinan, Shandong, 250022, China
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
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Experimental Characterization of Optimized Piezoelectric Energy Harvesters for Wearable Sensor Networks. SENSORS 2021; 21:s21217042. [PMID: 34770349 PMCID: PMC8587679 DOI: 10.3390/s21217042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/17/2021] [Accepted: 10/20/2021] [Indexed: 11/24/2022]
Abstract
The development of wearable devices and remote sensor networks progressively relies on their increased power autonomy, which can be further expanded by replacing conventional power sources, characterized by limited lifetimes, with energy harvesting systems. Due to its pervasiveness, kinetic energy is considered as one of the most promising energy forms, especially when combined with the simple and scalable piezoelectric approach. The integration of piezoelectric energy harvesters, generally in the form of bimorph cantilevers, with wearable and remote sensors, highlighted a drawback of such a configuration, i.e., their narrow operating bandwidth. In order to overcome this disadvantage while maximizing power outputs, optimized cantilever geometries, developed using the design of experiments approach, are analysed and combined in this work with frequency up-conversion excitation that allows converting random kinetic ambient motion into a periodical excitation of the harvester. The developed optimised designs, all with the same harvesters’ footprint area of 23 × 15 mm, are thoroughly analysed via coupled harmonic and transient numerical analyses, along with the mostly neglected strength analyses. The models are validated experimentally via innovative experimental setups. The thus-proposed ϕ = 50 mm watch-like prototype allows, by using a rotating flywheel, the collection of low-frequency (ca. 1 to 3 Hz) human kinetic energy, and the periodic excitation of the optimized harvesters that, oscillating at their eigenfrequencies (~325 to ~930 Hz), display specific power outputs improved by up to 5.5 times, when compared to a conventional rectangular form, with maximal power outputs of up to >130 mW and average power outputs of up to >3 mW. These power levels should amply satisfy the requirements of factual wearable medical systems, while providing also an adaptability to accommodate several diverse sensors. All of this creates the preconditions for the development of novel autonomous wearable devices aimed not only at sensor networks for remote patient monitoring and telemedicine, but, potentially, also for IoT and structural health monitoring.
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Nthubu B. An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions. Healthcare (Basel) 2021; 9:1329. [PMID: 34683009 PMCID: PMC8544449 DOI: 10.3390/healthcare9101329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/17/2021] [Accepted: 09/27/2021] [Indexed: 11/17/2022] Open
Abstract
The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and 'bedraggled daisy' Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.
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Affiliation(s)
- Badziili Nthubu
- Imagination Lancaster, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
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Rao P, Seshadri DR, Hsu JJ. Current and Potential Applications of Wearables in Sports Cardiology. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2021; 23:65. [PMID: 36213377 PMCID: PMC9536770 DOI: 10.1007/s11936-021-00942-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/18/2021] [Indexed: 12/17/2022]
Abstract
Purpose of the review Commercial wearable biosensors are commonly used among athletes and highly active individuals, although their value in sports cardiology is not well established. In this review, we discuss the evidence for the current applications of wearables and provide our outlook for promising future directions of this emerging field. Recent findings The integration of routine assessment of physiological parameters, activity data, and features such as electrocardiogram recording has generated excitement over a role for wearables to help diagnose and monitor cardiovascular disease. Presently, however, there are significant challenges limiting their routine clinical use. While studies suggest that wearable-derived data may help guide training, evidence for the use of wearables in guiding exercise regimens for individuals with cardiovascular disease is lacking. Further, there is a paucity of data to demonstrate its efficacy in detecting exercise-related arrhythmias or conditions associated with sudden cardiac death. Further technological developments may lead to a greater potential for wearables to aid in sports cardiology practice. Summary The ability to collect vast amounts of physiological information can help athletes personalize training regimens. However, interpretation of these data and separating the signal from the noise are paramount, especially when used in a clinical setting. While there are currently no standardized approaches for the use of wearable-derived data in sports cardiology, we outline three domains in which they could guide the care of athletes in the future: (1) optimizing athletic performance (2) guiding exercise in athletes with known cardiovascular disease, and (3) screening for cardiovascular disease.
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Affiliation(s)
- Prashant Rao
- Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Dhruv R. Seshadri
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Jeffrey J. Hsu
- Division of Cardiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
- Division of Cardiology, Veteran Affairs Greater Los Angeles Healthcare System, Los Angeles, CA
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Loeza-Mejía CI, Sánchez-DelaCruz E, Pozos-Parra P, Landero-Hernández LA. The potential and challenges of Health 4.0 to face COVID-19 pandemic: a rapid review. HEALTH AND TECHNOLOGY 2021; 11:1321-1330. [PMID: 34603926 PMCID: PMC8477175 DOI: 10.1007/s12553-021-00598-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/14/2021] [Indexed: 11/05/2022]
Abstract
The COVID-19 pandemic has generated the need to evolve health services to reduce the risk of contagion and promote a collaborative environment even remotely. Advances in Industry 4.0, including the internet of things, mobile networks, cloud computing, and artificial intelligence make Health 4.0 possible to connect patients with healthcare professionals. Hence, the focus of this work is analyzing the potentiality, and challenges of state-of-the-art Health 4.0 applications to face the COVID-19 pandemic including augmented environments, diagnosis of the virus, forecasts, medical robotics, and remote clinical services. It is concluded that Health 4.0 can be applied in the prevention of contagion, improve diagnosis, promote virtual learning environments, and offer remote services. However, there are still ethical, technical, security, and legal challenges to be addressed. Additionally, more imaging datasets for COVID-19 detection need to be made available to the scientific community. Working in the areas of opportunity will help to address the new normal. Likewise, Health 4.0 can be applied not only in the COVID-19 pandemic, but also in future global viruses and natural disasters.
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Greshake Tzovaras B, Senabre Hidalgo E, Alexiou K, Baldy L, Morane B, Bussod I, Fribourg M, Wac K, Wolf G, Ball M. Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study. J Med Internet Res 2021; 23:e28116. [PMID: 34505836 PMCID: PMC8463949 DOI: 10.2196/28116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/16/2021] [Accepted: 07/05/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. OBJECTIVE This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. METHODS We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. RESULTS The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. CONCLUSIONS As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.
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Affiliation(s)
- Bastian Greshake Tzovaras
- Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, Paris, France
- Open Humans Foundation, Sanford, NC, United States
| | - Enric Senabre Hidalgo
- Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, Paris, France
| | | | | | | | - Ilona Bussod
- Center for Research & Interdisciplinarity, Paris, France
| | | | - Katarzyna Wac
- Quality of Life Technologies, GSEM/CUI, University of Geneva, Geneva, Switzerland
| | - Gary Wolf
- Article 27 Foundation, Berkeley, CA, United States
| | - Mad Ball
- Open Humans Foundation, Sanford, NC, United States
- Center for Research & Interdisciplinarity, Paris, France
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Bayoumy K, Gaber M, Elshafeey A, Mhaimeed O, Dineen EH, Marvel FA, Martin SS, Muse ED, Turakhia MP, Tarakji KG, Elshazly MB. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol 2021; 18:581-599. [PMID: 33664502 PMCID: PMC7931503 DOI: 10.1038/s41569-021-00522-7] [Citation(s) in RCA: 231] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 01/31/2023]
Abstract
Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.
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Affiliation(s)
- Karim Bayoumy
- Department of Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA
| | - Mohammed Gaber
- Department of Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | | | - Omar Mhaimeed
- Department of Medical Education, Weill Cornell Medicine, Doha, Qatar
| | - Elizabeth H Dineen
- Department of Cardiovascular Medicine, University of California Irvine, Irvine, CA, USA
| | - Francoise A Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Seth S Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Evan D Muse
- Scripps Research Translational Institute and Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA, USA
| | - Mintu P Turakhia
- Center for Digital Health, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mohamed B Elshazly
- Department of Medical Education, Weill Cornell Medicine, Doha, Qatar.
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA.
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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Rentz LE, Ulman HK, Galster SM. Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep. SENSORS (BASEL, SWITZERLAND) 2021; 21:5071. [PMID: 34372308 PMCID: PMC8348972 DOI: 10.3390/s21155071] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/09/2021] [Accepted: 07/23/2021] [Indexed: 01/07/2023]
Abstract
Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to "measure" sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.
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Affiliation(s)
| | | | - Scott M. Galster
- Human Performance Innovation Center, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26505, USA; (L.E.R.); (H.K.U.)
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Zhang Y, Mei HX, Cao Y, Yan XH, Yan J, Gao HL, Luo HW, Wang SW, Jia XD, Kachalova L, Yang J, Xue SC, Zhou CG, Wang LX, Gui YH. Recent advances and challenges of electrode materials for flexible supercapacitors. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2021.213910] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Lee KFA, Gan WS, Christopoulos G. Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective. SENSORS 2021; 21:s21113843. [PMID: 34199416 PMCID: PMC8199616 DOI: 10.3390/s21113843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 01/14/2023]
Abstract
Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.
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Affiliation(s)
- Kar Fye Alvin Lee
- Smart Nation Translational Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;
- Correspondence:
| | - Woon-Seng Gan
- Smart Nation Translational Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Georgios Christopoulos
- Decision, Environmental and Organizational Neuroscience Lab (DeonLab), Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore;
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Hunt B, Ruiz AJ, Pogue BW. Smartphone-based imaging systems for medical applications: a critical review. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200421VR. [PMID: 33860648 PMCID: PMC8047775 DOI: 10.1117/1.jbo.26.4.040902] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 03/29/2021] [Indexed: 05/15/2023]
Abstract
SIGNIFICANCE Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed. AIM Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances. APPROACH First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed. RESULTS The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo, multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications. CONCLUSIONS A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forward.
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Affiliation(s)
- Brady Hunt
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
- Address all correspondence to Brady Hunt,
| | - Alberto J. Ruiz
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
| | - Brian W. Pogue
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
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An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations. SENSORS 2021; 21:s21051777. [PMID: 33806438 PMCID: PMC7961751 DOI: 10.3390/s21051777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 11/28/2022]
Abstract
Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.
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Sharma A, Badea M, Tiwari S, Marty JL. Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. Molecules 2021; 26:748. [PMID: 33535493 PMCID: PMC7867046 DOI: 10.3390/molecules26030748] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/24/2021] [Accepted: 01/26/2021] [Indexed: 12/18/2022] Open
Abstract
With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.
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Affiliation(s)
- Atul Sharma
- School of Chemistry, Monash University, Clayton, Melbourne, VIC 3800, Australia
- Department of Pharmaceutical Chemistry, SGT College of Pharmacy, SGT University, Budhera, Gurugram, Haryana 122505, India
| | - Mihaela Badea
- Fundamental, Prophylactic and Clinical Specialties Department, Faculty of Medicine, Transilvania University of Brasov, 500036 Brasov, Romania;
| | - Swapnil Tiwari
- School of Studies in Chemistry, Pt Ravishankar Shukla University, Raipur, CHATTISGARH 492010, India;
| | - Jean Louis Marty
- University of Perpignan via Domitia, 52 Avenue Paul Alduy, CEDEX 9, 66860 Perpignan, France
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Cho PJ, Singh K, Dunn J. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00009-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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50
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Sports medicine: bespoke player management. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00021-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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