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Lungu N, Popescu DE, Jura AMC, Zaharie M, Jura MA, Roșca I, Boia M. Enhancing Early Detection of Sepsis in Neonates through Multimodal Biosignal Integration: A Study of Pulse Oximetry, Near-Infrared Spectroscopy (NIRS), and Skin Temperature Monitoring. Bioengineering (Basel) 2024; 11:681. [PMID: 39061763 PMCID: PMC11273471 DOI: 10.3390/bioengineering11070681] [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: 05/26/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.
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Affiliation(s)
- Nicoleta Lungu
- Department of Obstetrics-Gynecology and Neonatology, University of Medicine and Pharmacy “Victor Babeș”, 300041 Timisoara, Romania; (N.L.)
- Department of Neonatology, “Louis Țurcanu” Children Emergency Clinical Hospital Timișoara, 300011 Timisoara, Romania
| | - Daniela-Eugenia Popescu
- Department of Obstetrics-Gynecology and Neonatology, University of Medicine and Pharmacy “Victor Babeș”, 300041 Timisoara, Romania; (N.L.)
- Department of Neonatology, Première Hospital, Regina Maria Health Network, 300645 Timisoara, Romania
| | - Ana Maria Cristina Jura
- Department of Obstetrics-Gynecology and Neonatology, University of Medicine and Pharmacy “Victor Babeș”, 300041 Timisoara, Romania; (N.L.)
- Department of Neonatology, Première Hospital, Regina Maria Health Network, 300645 Timisoara, Romania
| | - Mihaela Zaharie
- Department of Obstetrics-Gynecology and Neonatology, University of Medicine and Pharmacy “Victor Babeș”, 300041 Timisoara, Romania; (N.L.)
- Department of Neonatology, “Louis Țurcanu” Children Emergency Clinical Hospital Timișoara, 300011 Timisoara, Romania
| | - Mihai-Andrei Jura
- Department of Health Evaluation and Promotion, Romanian National Public Health Institute, 300226 Timisoara, Romania
| | - Ioana Roșca
- Neonatology Department, Clinical Hospital of Obstetrics and Gynecology, 060251 Bucharest, Romania
- Faculty of Midwifery and Nursery, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, Romania
| | - Mărioara Boia
- Department of Obstetrics-Gynecology and Neonatology, University of Medicine and Pharmacy “Victor Babeș”, 300041 Timisoara, Romania; (N.L.)
- Department of Neonatology, “Louis Țurcanu” Children Emergency Clinical Hospital Timișoara, 300011 Timisoara, Romania
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2
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Gao B, Jiang J, Zhou S, Li J, Zhou Q, Li X. Toward the Next Generation Human-Machine Interaction: Headworn Wearable Devices. Anal Chem 2024; 96:10477-10487. [PMID: 38888091 DOI: 10.1021/acs.analchem.4c01190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Wearable devices are lightweight and portable devices worn directly on the body or integrated into the user's clothing or accessories. They are usually connected to the Internet and combined with various software applications to monitor the user's physical conditions. The latest research shows that wearable head devices, particularly those incorporating microfluidic technology, enable the monitoring of bodily fluids and physiological states. Here, we summarize the main forms, functions, and applications of head wearable devices through innovative researches in recent years. The main functions of wearable head devices are sensor monitoring, diagnosis, and even therapeutic interventions. Through this application, real-time monitoring of human physiological conditions and noninvasive treatment can be realized. Furthermore, microfluidics can realize real-time monitoring of body fluids and skin interstitial fluid, which is highly significant in medical diagnosis and has broad medical application prospects. However, despite the progress made, significant challenges persist in the integration of microfluidics into wearable devices at the current technological level. Herein, we focus on summarizing the cutting-edge applications of microfluidic contact lenses and offer insights into the burgeoning intersection between microfluidics and head-worn wearables, providing a glimpse into their future prospects.
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Affiliation(s)
- Bingbing Gao
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Jingwen Jiang
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Shu Zhou
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Jun Li
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Qian Zhou
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
| | - Xin Li
- School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, P. R. China
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Zhang X, Zhang Y, Si Y, Gao N, Zhang H, Yang H. A high altitude respiration and SpO2 dataset for assessing the human response to hypoxia. Sci Data 2024; 11:248. [PMID: 38413602 PMCID: PMC10899206 DOI: 10.1038/s41597-024-03065-x] [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: 06/14/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024] Open
Abstract
This report presents the Harespod dataset, an open dataset for high altitude hypoxia research, which includes respiration and SpO2 data. The dataset was collected from 15 college students aged 23-31 in a hypobaric oxygen chamber, during simulated altitude changes and induced hypoxia. Real-time physiological data, such as oxygen saturation waveforms, oxygen saturation, respiratory waveforms, heart rate, and pulse rate, were obtained at 100 Hz. Approximately 12 hours of valid data were collected from all participants. Researchers can easily identify the altitude corresponding to physiological signals based on their inherent patterns. Time markers were also recorded during altitude changes to facilitate realistic annotation of physiological signals and analysis of time-difference-of-arrival between various physiological signals for the same altitude change event. In high altitude scenarios, this dataset can be used to enhance the detection of human hypoxia states, predict respiratory waveforms, and develop related hardware devices. It will serve as a valuable and standardized resource for researchers in the field of high altitude hypoxia research, enabling comprehensive analysis and comparison.
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Affiliation(s)
- Xi Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
| | - Yingjun Si
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Nan Gao
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Honghao Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China.
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4
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Pittella E, Testa O, Podestà L, Piuzzi E. An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices. SENSORS (BASEL, SWITZERLAND) 2024; 24:1008. [PMID: 38339729 PMCID: PMC10857427 DOI: 10.3390/s24031008] [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: 12/29/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 02/12/2024]
Abstract
(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.
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Affiliation(s)
- Erika Pittella
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
| | - Orlandino Testa
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
| | - Luca Podestà
- Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy;
| | - Emanuele Piuzzi
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
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Davies HJ, Hammour G, Xiao H, Bachtiger P, Larionov A, Molyneaux PL, Peters NS, Mandic DP. Physically Meaningful Surrogate Data for COPD. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:148-156. [PMID: 38487098 PMCID: PMC10939325 DOI: 10.1109/ojemb.2024.3360688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/23/2023] [Accepted: 01/26/2024] [Indexed: 03/17/2024] Open
Abstract
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
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Affiliation(s)
- Harry J. Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Hongjian Xiao
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Patrik Bachtiger
- National Heart and Lung InstituteImperial College LondonSW7 2BXLondonU.K.
| | | | | | - Nicholas S. Peters
- National Heart and Lung InstituteImperial College LondonSW7 2BXLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
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6
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Pascual-Saldaña H, Masip-Bruin X, Asensio A, Alonso A, Blanco I. Innovative Predictive Approach towards a Personalized Oxygen Dosing System. SENSORS (BASEL, SWITZERLAND) 2024; 24:764. [PMID: 38339481 PMCID: PMC10857553 DOI: 10.3390/s24030764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Despite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities which patients engage in. To address this challenge, we propose a software architecture aimed at delivering patient-personalized edge-based artificial intelligence (AI)-assisted models that are built upon data collected from patients' previous experiences along with an evaluation function. The main objectives reside in proactively administering precise oxygen dosages in real time to the patient (the edge), leveraging individual patient data, previous experiences, and actual activity levels, thereby representing a substantial advancement over conventional oxygen dosing. Through a pilot test using vital sign data from a cohort of five patients, the limitations of a one-size-fits-all approach are demonstrated, thus highlighting the need for personalized treatment strategies. This study underscores the importance of adopting advanced technological approaches for ambulatory oxygen therapy.
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Affiliation(s)
- Heribert Pascual-Saldaña
- Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain;
| | - Xavi Masip-Bruin
- Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain;
| | - Adrián Asensio
- Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain;
| | - Albert Alonso
- Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain;
| | - Isabel Blanco
- Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, 08036 Barcelona, Spain;
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7
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Parupelli SK, Desai S. The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects. Bioengineering (Basel) 2023; 11:32. [PMID: 38247910 PMCID: PMC10813523 DOI: 10.3390/bioengineering11010032] [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: 11/20/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain.
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Affiliation(s)
- Santosh Kumar Parupelli
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA;
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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8
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Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
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Sasaki S, Sugita N, Terai T, Yoshizawa M. Non-Contact Measurement of Blood Oxygen Saturation Using Facial Video Without Reference Values. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:76-83. [PMID: 38088997 PMCID: PMC10712673 DOI: 10.1109/jtehm.2023.3318643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/05/2023] [Accepted: 09/20/2023] [Indexed: 12/18/2023]
Abstract
The continuous measurement of percutaneous oxygen saturation (SpO2) enables diseases that cause hypoxemia to be detected early and patients' conditions to be monitored. Currently, SpO2 is mainly measured using a pulse oximeter, which, owing to its simplicity, can be used in clinical settings and at home. However, the pulse oximeter requires a sensor to be in contact with the skin; therefore, prolonged use of the pulse oximeter for neonates or patients with sensitive skin may cause local inflammation or stress due to restricted movement. In addition, owing to COVID-19, there has been a growing demand for the contactless measurement of SpO2. Several studies on measuring SpO2 without contact used skin video images have been conducted. However, in these studies, the SpO2 values were estimated using a linear regression model or a look-up table that required reference values obtained using a contact-type pulse oximeter. In this study, we propose a new technique for the contactless measurement of SpO2 that does not require reference values. Specifically, we used certain approaches that reduced the influence of non-pulsating components and utilized different light wavelengths of video images that penetrated subcutaneously to different depths. We experimentally investigated the accuracy of SpO2 measurements using the proposed methods. The results indicate that the proposed methods were more accurate than the conventional method.
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Affiliation(s)
- Soma Sasaki
- Graduate School of EngineeringTohoku UniversitySendai9808579Japan
| | - Norihiro Sugita
- Graduate School of EngineeringTohoku UniversitySendai9808579Japan
- Cyberscience CenterTohoku UniversitySendai9808579Japan
| | - Takanori Terai
- Graduate School of EngineeringTohoku UniversitySendai9808579Japan
| | - Makoto Yoshizawa
- Center for Promotion of Innovation StrategyTohoku UniversitySendai9800845Japan
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10
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Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
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Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
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11
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Osama M, Ateya AA, Sayed MS, Hammad M, Pławiak P, Abd El-Latif AA, Elsayed RA. Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions. SENSORS (BASEL, SWITZERLAND) 2023; 23:7435. [PMID: 37687891 PMCID: PMC10490658 DOI: 10.3390/s23177435] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/15/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.
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Affiliation(s)
- Manar Osama
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
| | - Abdelhamied A. Ateya
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
| | - Mohammed S. Sayed
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
- Department of Electronics and Communication Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
| | - Mohamed Hammad
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Ahmed A. Abd El-Latif
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom 32511, Egypt
| | - Rania A. Elsayed
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
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12
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Azudin K, Gan KB, Jaafar R, Ja'afar MH. The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6484. [PMID: 37514778 PMCID: PMC10384007 DOI: 10.3390/s23146484] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/29/2023] [Accepted: 06/04/2023] [Indexed: 07/30/2023]
Abstract
Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.
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Affiliation(s)
- Khalida Azudin
- Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Kok Beng Gan
- Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Rosmina Jaafar
- Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mohd Hasni Ja'afar
- Department of Community Health, Faculty of Medicine, UKM Medical Centre, Universiti Kebangsaan Malaysia, Cheras 56000, Kuala Lumpur, Malaysia
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Tian H, Occhipinti E, Nassibi A, Mandic DP. Hearables: Heart Rate Variability from Ear Electrocardiogram and Ear Photoplethysmogram (Ear-ECG and Ear-PPG). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082712 DOI: 10.1109/embc40787.2023.10340371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
This work aims to classify physiological states using heart rate variability (HRV) features extracted from electrocardiograms recorded in the ears (ear-ECG). The physiological states considered in this work are: (a) normal breathing, (b) controlled slow breathing, and (c) mental exercises. Since both (b) and (c) cause higher variance in heartbeat intervals, breathing-related features (SpO2 and mean breathing interval) from the ear Photoplethysmogram (ear-PPG) are used to facilitate classification. This work: 1) proposes a scheme that, after initialization, automatically extracts R-peaks from low signal-to-noise ratio ear-ECG; 2) verifies the feasibility of extracting meaningful HRV features from ear-ECG; 3) quantitatively compares several ear-ECG sites; and 4) discusses the benefits of combining ear-ECG and ear-PPG features.
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14
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Wu KC, Martin A, Renna M, Robinson M, Ozana N, Carp SA, Franceschini MA. Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography. NEUROPHOTONICS 2023; 10:035008. [PMID: 37680339 PMCID: PMC10482352 DOI: 10.1117/1.nph.10.3.035008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023]
Abstract
Significance Combining near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) allows for quantifying cerebral blood volume, flow, and oxygenation changes continuously and non-invasively. As recently shown, the DCS pulsatile cerebral blood flow index (pCBF i ) can be used to quantify critical closing pressure (CrCP) and cerebrovascular resistance (CVR i ). Aim Although current DCS technology allows for reliable monitoring of the slow hemodynamic changes, resolving pulsatile blood flow at large source-detector separations, which is needed to ensure cerebral sensitivity, is challenging because of its low signal-to-noise ratio (SNR). Cardiac-gated averaging of several arterial pulse cycles is required to obtain a meaningful waveform. Approach Taking advantage of the high SNR of NIRS, we demonstrate a method that uses the NIRS photoplethysmography (NIRS-PPG) pulsatile signal to model DCS pCBF i , reducing the coefficient of variation of the recovered pulsatile waveform (pCBF i - fit ) and allowing for an unprecedented temporal resolution (266 Hz) at a large source-detector separation (> 3 cm ). Results In 10 healthy subjects, we verified the quality of the NIRS-PPG pCBF i - fit during common tasks, showing high fidelity against pCBF i (R 2 0.98 ± 0.01 ). We recovered CrCP and CVR i at 0.25 Hz, > 10 times faster than previously achieved with DCS. Conclusions NIRS-PPG improves DCS pCBF i SNR, reducing the number of gate-averaged heartbeats required to recover CrCP and CVR i .
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Affiliation(s)
- Kuan Cheng Wu
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Alyssa Martin
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Marco Renna
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Mitchell Robinson
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Nisan Ozana
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
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15
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Davies HJ, Zylinski M, Bermond M, Liu Z, Khaleghimeybodi M, Mandic DP. Feasibility of Transfer Learning from Finger PPG to In-Ear PPG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083651 DOI: 10.1109/embc40787.2023.10340172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The success of deep learning methods has enabled many modern wearable health applications, but has also highlighted the critical caveat of their extremely data hungry nature. While the widely explored wrist and finger photoplethysmography (PPG) sites are less affected, given the large available databases, this issue is prohibitive to exploring the full potential of novel recording locations such as in-ear wearables. To this end, we assess the feasibility of transfer learning from finger PPG to in-ear PPG in the context of deep learning for respiratory monitoring. This is achieved by introducing an encoder-decoder framework which is set up to extract respiratory waveforms from PPG, whereby simultaneously recorded gold standard respiratory waveforms (capnography, impedance pneumography and air flow) are used as a training reference. Next, the data augmentation and training pipeline is examined for both training on finger PPG and the subsequent fine tuning on in-ear PPG. The results indicate that, through training on two large finger PPG data sets (95 subjects) and then retraining on our own small in-ear PPG data set (6 subjects), the model achieves lower and more consistent test error for the prediction of the respiratory waveforms, compared to training on the small in-ear data set alone. This conclusively demonstrates the feasibility of transfer learning from finger PPG to in-ear PPG, leading to better generalisation across a wide range of respiratory rates.
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16
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Bermond M, Davies HJ, Occhipinti E, Nassibi A, Mandic DP. Reducing racial bias in SpO 2 estimation: The effects of skin pigmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083781 DOI: 10.1109/embc40787.2023.10341069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Accurate pulse-oximeter readings are critical for clinical decisions, especially when arterial blood-gas tests - the gold standard for determining oxygen saturation levels - are not available, such as when determining COVID-19 severity. Several studies demonstrate that pulse oxygen saturation estimated from photoplethysmography (PPG) introduces a racial bias due to the more profound scattering of light in subjects with darker skin due to the increased presence of melanin. This leads to an overestimation of blood oxygen saturation in those with darker skin that is increased for low blood oxygen levels and can result in a patient not receiving potentially life-saving supplemental oxygen. This racial bias has been comprehensively studied in conventional finger pulse oximetry but in other less commonly used measurement sites, such as in-ear pulse oximetry, it remains unexplored. Different measurement sites can have thinner epidermis compared with the finger and lower exposure to sunlight (such as is the case with the ear canal), and we hypothesise that this could reduce the bias introduced by skin tone on pulse oximetry. To this end, we compute SpO2 in different body locations, during rest and breath-holds, and compare with the index finger. The study involves a participant pool covering 6-pigmentation categories from Fitzpatrick's Skin Pigmentation scale. These preliminary results indicate that locations characterized by cartilaginous highly vascularized tissues may be less prone to the influence of melanin and pigmentation in the estimation of SpO2, paving the way for the development of non-discriminatory pulse oximetry devices.
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17
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Hammour G, Mandic DP. An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063319. [PMID: 36992029 PMCID: PMC10057625 DOI: 10.3390/s23063319] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 06/12/2023]
Abstract
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.
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18
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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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Choi JY, Jeon S, Kim H, Ha J, Jeon GS, Lee J, Cho SI. Health-Related Indicators Measured Using Earable Devices: Systematic Review. JMIR Mhealth Uhealth 2022; 10:e36696. [PMID: 36239201 PMCID: PMC9709679 DOI: 10.2196/36696] [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: 01/21/2022] [Revised: 09/23/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. OBJECTIVE Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. METHODS A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. RESULTS A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). CONCLUSIONS Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.
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Affiliation(s)
- Jin-Young Choi
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Seonghee Jeon
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Hana Kim
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jaeyoung Ha
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Gyeong-Suk Jeon
- Department of Nursing, College of Natural Science, Mokpo National University, Mokpo, Republic of Korea
| | - Jeong Lee
- Department of Nursing, College of Health and Medical Science, Chodang University, Muan, Republic of Korea
| | - Sung-Il Cho
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
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20
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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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21
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Olsen F, Suyderhoud JP, Khanna AK. Respiratory monitoring of nonintubated patients in nonoperating room settings: old and new technologies. Curr Opin Anaesthesiol 2022; 35:521-527. [PMID: 35788554 DOI: 10.1097/aco.0000000000001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Postoperative mortality in the 30 days after surgery remains disturbingly high. Inadequate, intermittent and incomplete monitoring of vital signs in the nonoperating room environment is common practice. The rise of nonoperating room anaesthesia and sedation outside the operating room has highlighted the need to develop new and robust methods of portable continuous respiratory monitoring. This review provides a summary of old and new technologies in this environment. RECENT FINDINGS Technical advances have made possible the utilization of established monitoring to extrapolate respiratory rate, the increased availability and user friendliness of side stream capnography and the advent of other innovative systems. The use of aggregate signals wherein different modalities compensate for individual shortcomings seem to provide a reliable and artefact-free system. SUMMARY Respiratory monitoring is required in several situations and patient categories outside the operating room. The chosen modality must be able to detect respiratory compromise in a timely and accurate manner. Combing several modalities in a nonobtrusive, nontethered system and having an integrated output seems to give a reliable and responsive signal.
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Affiliation(s)
- Fredrik Olsen
- Department of Anesthesiology, Wake Forest School of Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
- Department of Anaesthesiology and Critical Care, Sahlgrenska University Hospital/Mölndal, Sweden
| | - Johan Pieter Suyderhoud
- Department of Anesthesiology, Wake Forest School of Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
| | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, Wake Forest School of Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
- Outcomes Research Consortium, Cleveland, Ohio, USA
- Perioperative Outcomes and Informatics Collaborative, Winston-Salem, North Carolina, USA
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22
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Davies HJ, Williams I, Mandic DP. Tracking Cognitive Workload in Gaming with In-Ear [Formula: see text]. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4913-4916. [PMID: 36085931 DOI: 10.1109/embc48229.2022.9871448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The feasibility of using in-ear [Formula: see text] to track cognitive workload induced by gaming is investigated. This is achieved by examining temporal variations in cognitive workload through the game Geometry Dash, with 250 trials across 7 subjects. The relationship between performance and cognitive load in Dark Souls III boss fights is also investigated followed by a comparison of the cognitive workload responses across three different genres of game. A robust decrease in in-ear [Formula: see text] is observed in response to cognitive workload induced by gaming, which is consistent with existing results from memory tasks. The results tentatively suggest that in-ear [Formula: see text] may be able to distinguish cognitive workload alone, whereas heart rate and breathing rate respond similarly to both cognitive workload and stress. This study demonstrates the feasibility of low cost wearable cognitive workload tracking in gaming with in-ear [Formula: see text], with applications to the play testing of games and biofeedback in games of the future.
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Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10030547. [PMID: 35327025 PMCID: PMC8950880 DOI: 10.3390/healthcare10030547] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023] Open
Abstract
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
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Affiliation(s)
- Malak Abdullah Almarshad
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
- Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
- Correspondence:
| | - Md Saiful Islam
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Ahmed S. BaHammam
- The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia;
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24
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Wearable Sensors for Vital Signs Measurement: A Survey. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2022. [DOI: 10.3390/jsan11010019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.
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25
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Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL. Remote Healthcare for Elderly People Using Wearables: A Review. BIOSENSORS 2022; 12:bios12020073. [PMID: 35200334 PMCID: PMC8869443 DOI: 10.3390/bios12020073] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/17/2022] [Accepted: 01/25/2022] [Indexed: 05/21/2023]
Abstract
The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.
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Affiliation(s)
- José Oscar Olmedo-Aguirre
- Department of Electrical Engineering, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2 508, Col. San Pedro Zacatenco, Delegación Gustavo A. Madero, Mexico City C.P. 07360, Mexico;
| | - Josimar Reyes-Campos
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
- Correspondence: ; Tel./Fax: +52-272-725-7056
| | - Isaac Machorro-Cano
- Universidad del Papaloapan, Circuito Central #200, Col. Parque Industrial, Tuxtepec C.P. 68301, Oaxaca, Mexico;
| | - Lisbeth Rodríguez-Mazahua
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico;
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Davies HJ, Bachtiger P, Williams I, Molyneaux PL, Peters NS, Mandic DP. Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD. IEEE Trans Biomed Eng 2022; 69:2390-2400. [PMID: 35077352 DOI: 10.1109/tbme.2022.3145688] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
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27
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Sondej T, Zawadzka S. Influence of cuff pressures of automatic sphygmomanometers on pulse oximetry measurements. MEASUREMENT : JOURNAL OF THE INTERNATIONAL MEASUREMENT CONFEDERATION 2022; 187:110329. [PMID: 34690401 PMCID: PMC8520452 DOI: 10.1016/j.measurement.2021.110329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/20/2021] [Accepted: 10/10/2021] [Indexed: 06/13/2023]
Abstract
Information about blood arterial oxygen saturation (SpO2) is crucial in critical care settings or home health monitoring during the COVID-19 pandemic. Also, we need to identify the factors that affect the SpO2 measurement. In this paper, the effect of compression of the cuff during noninvasive blood pressure (NIBP) measurement on the SpO2 results was investigated. A custom-made system was used for simultaneous measurement of NIBP and SpO2. The study was conducted on 213 subjects aged between 21 and 93, with a systolic blood pressure of (94 to 194) mmHg, diastolic blood pressure of (52-98) mmHg, and 994 NIBP readings were used for the analysis. During the NIBP measurement, momentary changes in SpO2 can reach ±17% and are in most cases positive (mean 2.9%). The change was not correlated with sex, age, height, body weight, BMI, HR and blood pressure. The obtained results show that frequent NIBP measurements may lead to wrong conclusions about SpO2. In our study, pressure measurements mainly caused the increase of blood oxygenation level.
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Affiliation(s)
- Tadeusz Sondej
- Faculty of Electronics, Military University of Technology, Warsaw, Poland
| | - Sylwia Zawadzka
- Faculty of Electronics, Military University of Technology, Warsaw, Poland
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28
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Chan M, Ganti VG, Heller JA, Abdallah CA, Etemadi M, Inan OT. Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum. BIOSENSORS 2021; 11:bios11120521. [PMID: 34940278 PMCID: PMC8699050 DOI: 10.3390/bios11120521] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 05/31/2023]
Abstract
In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.
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Affiliation(s)
- Michael Chan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
| | - Venu G. Ganti
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - J. Alex Heller
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (J.A.H.); (M.E.)
| | - Calvin A. Abdallah
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
| | - Mozziyar Etemadi
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (J.A.H.); (M.E.)
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60201, USA
| | - Omer T. Inan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (C.A.A.)
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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29
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Ne CKH, Muzaffar J, Amlani A, Bance M. Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review. Expert Rev Med Devices 2021; 18:95-128. [PMID: 34904507 DOI: 10.1080/17434440.2021.2014321] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Hearables are ear devices used for multiple purposes including ubiquitous/remote monitoring of vital signals. This can support early detection, prevention, and management of urgent/non-urgent healthcare needs. This review therefore seeks to analyse the challenges and capabilities of hearables used to monitor human physiological signals. AREAS COVERED Studies were identified via search (Medline, Embase, Web of Science, Cochrane Library, Scopus) and conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Bias assessment used the Mixed Methods Appraisal Tool 2018 and Quality Assessment of Diagnostic Accuracy Studies 2nd Edition. 92/631 studies met the inclusion criteria and were qualitatively analysed. The outcomes, applications, advantages and limitations were discussed according to the vital signal measured. The bias risk ranged from low to high, with most studies facing moderate to high risk in subject selection due to small sample sizes. EXPERT OPINION : Most studies reported good outcomes for ear signal acquisition compared to reference devices. To improve practicability and implementation, wireless connectivity, battery life, impact of motion/environmental artifacts and comfort need to be addressed going forward. Hearable technologies have also shown potential synergies with hearing aids. In future, multimodal ear-sensing devices opens the possibility of comprehensive health monitoring within daily life.
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Affiliation(s)
| | - Jameel Muzaffar
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Aakash Amlani
- Department of Ear, Nose and Throat Surgery, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Manohar Bance
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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30
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Hammour GM, Mandic DP. Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6889-6893. [PMID: 34892689 DOI: 10.1109/embc46164.2021.9629886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ear-worn devices are rapidly gaining popularity as they provide the means for measuring vital signals in an unobtrusive, 24/7 wearable and discrete fashion. Naturally, these devices are prone to motion artefacts when used in out-of-lab environments, various movements and activities cause relative movement between user's skin and the electrodes. Historically, these artefacts are seen as nuisance resulting in discarding the segments of signal wherever such artefacts are present. In this work, we make use of such artefacts to classify different daily activities that include sitting, speaking aloud, chewing and walking. To this end, multiple classification techniques are employed to identify these activities using 8 features calculated from the electrode and microphone signal embedded in a generic multimodal in-ear sensor. The results show an overall training accuracy of 93% and 90% and a testing accuracy of 85% and 80% when using a KNN and a 2-layer neural network respectively, thus providing a much needed, simple and reliable framework for real-life human activity classification.
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31
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Batista E, Moncusi MA, López-Aguilar P, Martínez-Ballesté A, Solanas A. Sensors for Context-Aware Smart Healthcare: A Security Perspective. SENSORS (BASEL, SWITZERLAND) 2021; 21:6886. [PMID: 34696099 PMCID: PMC8537585 DOI: 10.3390/s21206886] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.
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Affiliation(s)
- Edgar Batista
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
- SIMPPLE S.L., C. Joan Maragall 1A, 43003 Tarragona, Spain
| | - M. Angels Moncusi
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Pablo López-Aguilar
- Anti-Phishing Working Group EU, Av. Diagonal 621–629, 08028 Barcelona, Spain;
| | - Antoni Martínez-Ballesté
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Agusti Solanas
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
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32
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Spang RP, Pieper K. The tiny effects of respiratory masks on physiological, subjective, and behavioral measures under mental load in a randomized controlled trial. Sci Rep 2021; 11:19601. [PMID: 34599253 PMCID: PMC8486780 DOI: 10.1038/s41598-021-99100-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 09/20/2021] [Indexed: 02/08/2023] Open
Abstract
Since the outbreak of the coronavirus disease (COVID-19), face coverings are recommended to diminish person-to-person transmission of the SARS-CoV-2 virus. Some public debates concern claims regarding risks caused by wearing face masks, like, e.g., decreased blood oxygen levels and impaired cognitive capabilities. The present, pre-registered study aims to contribute clarity by delivering a direct comparison of wearing an N95 respirator and wearing no face covering. We focused on a demanding situation to show that cognitive efficacy and individual states are equivalent in both conditions. We conducted a randomized-controlled crossover trial with 44 participants. Participants performed the task while wearing an N95 FFR versus wearing none. We measured physiological (blood oxygen saturation and heart rate variability), behavioral (parameters of performance in the task), and subjective (perceived mental load) data to substantiate our assumption as broadly as possible. We analyzed data regarding both statistical equivalence and differences. All of the investigated dimensions showed statistical equivalence given our pre-registered equivalence boundaries. None of the dimensions showed a significant difference between wearing an FFR and not wearing an FFR.Trial Registration: Preregistered with the Open Science Framework: https://osf.io/c2xp5 (15/11/2020). Retrospectively registered with German Clinical Trials Register: DRKS00024806 (18/03/2021).
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Affiliation(s)
- Robert P Spang
- Quality and Usability Lab, Institute of Software Engineering and Theoretical Computer Science, Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany.
| | - Kerstin Pieper
- Quality and Usability Lab, Institute of Software Engineering and Theoretical Computer Science, Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany
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33
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Rdest M, Janas D. Carbon Nanotube Wearable Sensors for Health Diagnostics. SENSORS (BASEL, SWITZERLAND) 2021; 21:5847. [PMID: 34502734 PMCID: PMC8433779 DOI: 10.3390/s21175847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/20/2021] [Accepted: 08/26/2021] [Indexed: 11/25/2022]
Abstract
This perspective article highlights a recent surge of interest in the application of textiles containing carbon nanotube (CNT) sensors for human health monitoring. Modern life puts more and more pressure on humans, which translates into an increased number of various health disorders. Unfortunately, this effect either decreases the quality of life or shortens it prematurely. A possible solution to this problem is to employ sensors to monitor various body functions and indicate an upcoming disease likelihood at its early stage. A broad spectrum of materials is currently under investigation for this purpose, some of which already entered the market. One of the most promising materials in this field are CNTs. They are flexible and of high electrical conductivity, which can be modulated upon several forms of stimulation. The article begins with an illustration of techniques for how wearable sensors can be built from them. Then, their application potential for tracking various health parameters is presented. Finally, the article ends with a summary of this field's progress and a vision of the key directions to domesticate this concept.
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Affiliation(s)
- Monika Rdest
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge CB3 0FS, UK;
| | - Dawid Janas
- Department of Organic Chemistry, Bioorganic Chemistry and Biotechnology, Silesian University of Technology, B. Krzywoustego 4, 44-100 Gliwice, Poland
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Channa A, Popescu N, Skibinska J, Burget R. The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:5787. [PMID: 34502679 PMCID: PMC8434481 DOI: 10.3390/s21175787] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/21/2021] [Accepted: 08/24/2021] [Indexed: 12/16/2022]
Abstract
The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.
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Affiliation(s)
- Asma Channa
- Computer Science Department, University POLITEHNICA of Bucharest, RO-060042 Bucharest, Romania
- DIIES Department, University Mediterranea of Reggio Calabria, 89100 Reggio Calabria, Italy
| | - Nirvana Popescu
- Computer Science Department, University POLITEHNICA of Bucharest, RO-060042 Bucharest, Romania
| | - Justyna Skibinska
- Department of Telecommunications, Brno University of Technology, 61600 Brno, Czech Republic; (J.S.); (R.B.)
- Unit of Electrical Engineering, Tampere University, 33720 Tampere, Finland
| | - Radim Burget
- Department of Telecommunications, Brno University of Technology, 61600 Brno, Czech Republic; (J.S.); (R.B.)
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Firouzi F, Farahani B, Daneshmand M, Grise K, Song J, Saracco R, Wang LL, Lo K, Angelov P, Soares E, Loh PS, Talebpour Z, Moradi R, Goodarzi M, Ashraf H, Talebpour M, Talebpour A, Romeo L, Das R, Heidari H, Pasquale D, Moody J, Woods C, Huang ES, Barnaghi P, Sarrafzadeh M, Li R, Beck KL, Isayev O, Sung N, Luo A. Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World. IEEE INTERNET OF THINGS JOURNAL 2021; 8:12826-12846. [PMID: 35782886 PMCID: PMC8769005 DOI: 10.1109/jiot.2021.3073904] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 03/09/2021] [Accepted: 04/02/2021] [Indexed: 05/07/2023]
Abstract
As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.
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Affiliation(s)
- Farshad Firouzi
- Electrical and Computer Engineering DepartmentDuke University Durham NC 27708 USA
| | - Bahar Farahani
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Mahmoud Daneshmand
- Business Intelligence and AnalyticsStevens Institute of Technology Hoboken NJ 07030 USA
| | - Kathy Grise
- IEEE Future Directions Piscataway NJ 08854 USA
| | - Jaeseung Song
- Department of Computer and Information SecuritySejong University Seoul 15600 South Korea
| | | | - Lucy Lu Wang
- Allen Institute for Artificial Intelligence Seattle WA 98112 USA
| | - Kyle Lo
- Allen Institute for Artificial Intelligence Seattle WA 98112 USA
| | - Plamen Angelov
- School of Computing and CommunicationsLancaster University Lancashire LA1 4YW U.K
| | - Eduardo Soares
- School of Computing and CommunicationsLancaster University Lancashire LA1 4YW U.K
| | - Po-Shen Loh
- Department of Mathematical SciencesCarnegie Mellon University Pittsburgh PA 15213 USA
| | - Zeynab Talebpour
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Reza Moradi
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Mohsen Goodarzi
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | | | | | - Alireza Talebpour
- Cyberspace Research Institute, Shahid Beheshti University Tehran 1983969411 Iran
| | - Luca Romeo
- Department of Information EngineeringUniversit Politecnica delle Marche 60121 Ancona Italy
| | - Rupam Das
- James Watt School of EngineeringUniversity of Glasgow Glasgow G12 8QQ U.K
| | - Hadi Heidari
- James Watt School of EngineeringUniversity of Glasgow Glasgow G12 8QQ U.K
| | - Dana Pasquale
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - James Moody
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Chris Woods
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Erich S Huang
- School of Medicine and Duke HealthDuke University Durham NC 27708 USA
| | - Payam Barnaghi
- Department of Brain SciencesImperial College London London SW7 2AZ U.K
- U.K. Dementia Research Institute London U.K
| | - Majid Sarrafzadeh
- Computer Science Department & Electrical and Computer Engineering DepartmentUniversity of California at Los Angeles Los Angeles CA 90095 USA
| | - Ron Li
- Department of MedicineStanford University School of Medicine Stanford CA 94305 USA
| | | | - Olexandr Isayev
- Department of ChemistryCarnegie Mellon University Pittsburgh PA 15213 USA
| | - Nakmyoung Sung
- Korea Electronics Technology Institute Seongnam 13509 South Korea
| | - Alan Luo
- Computer Science DepartmentStanford University Stanford CA 94305 USA
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Abstract
Despite the increasing awareness of the importance of sleep, the number of people suffering from insufficient sleep has increased every year. The gold-standard sleep assessment uses polysomnography (PSG) with various sensors to identify sleep patterns and disorders. However, due to the high cost of PSG and limited availability, many people with sleep disorders are left undiagnosed. Recent wearable sensors and electronics enable portable, continuous monitoring of sleep at home, overcoming the limitations of PSG. This report reviews the advances in wearable sensors, miniaturized electronics, and system packaging for home sleep monitoring. New devices available in the market and systems are collectively summarized based on their overall structure, form factor, materials, and sleep assessment method. It is expected that this review provides a comprehensive view of newly developed technologies and broad insights on wearable sensors and portable electronics toward advanced sleep monitoring as well as at-home sleep assessment.
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Affiliation(s)
- Shinjae Kwon
- George W. Woodruff School of Mechanical Engineering, Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hojoong Kim
- George W. Woodruff School of Mechanical Engineering, Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Neural Engineering Center, Flexible and Wearable Electronics Advanced Research, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA
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Photoplethysmography in Normal and Pathological Sleep. SENSORS 2021; 21:s21092928. [PMID: 33922042 PMCID: PMC8122413 DOI: 10.3390/s21092928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 01/20/2023]
Abstract
This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.
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