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Li S, Li H, Lu Y, Zhou M, Jiang S, Du X, Guo C. Advanced Textile-Based Wearable Biosensors for Healthcare Monitoring. BIOSENSORS 2023; 13:909. [PMID: 37887102 PMCID: PMC10605256 DOI: 10.3390/bios13100909] [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: 08/28/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/28/2023]
Abstract
With the innovation of wearable technology and the rapid development of biosensors, wearable biosensors based on flexible textile materials have become a hot topic. Such textile-based wearable biosensors promote the development of health monitoring, motion detection and medical management, and they have become an important support tool for human healthcare monitoring. Textile-based wearable biosensors not only non-invasively monitor various physiological indicators of the human body in real time, but they also provide accurate feedback of individual health information. This review examines the recent research progress of fabric-based wearable biosensors. Moreover, materials, detection principles and fabrication methods for textile-based wearable biosensors are introduced. In addition, the applications of biosensors in monitoring vital signs and detecting body fluids are also presented. Finally, we also discuss several challenges faced by textile-based wearable biosensors and the direction of future development.
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Affiliation(s)
- Sheng Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
- CCZU-ARK Institute of Carbon Materials, Nanjing 210012, China
| | - Huan Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Yongcai Lu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Minhao Zhou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Xiaosong Du
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China; (S.L.); (H.L.); (Y.L.); (M.Z.); (S.J.)
| | - Chang Guo
- CCZU-ARK Institute of Carbon Materials, Nanjing 210012, China
- School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
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Laufer B, Docherty PD, Murray R, Krueger-Ziolek S, Jalal NA, Hoeflinger F, Rupitsch SJ, Reindl L, Moeller K. Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression. SENSORS (BASEL, SWITZERLAND) 2023; 23:7407. [PMID: 37687863 PMCID: PMC10490437 DOI: 10.3390/s23177407] [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: 07/24/2023] [Revised: 08/18/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.
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Affiliation(s)
- Bernhard Laufer
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Paul D. Docherty
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
| | - Rua Murray
- School of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand
| | - Sabine Krueger-Ziolek
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Nour Aldeen Jalal
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04109 Leipzig, Germany
| | - Fabian Hoeflinger
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Stefan J. Rupitsch
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Leonhard Reindl
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Knut Moeller
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
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Laufer B, Jalal NA, Krueger-Ziolek S, Docherty PD, Murray R, Hoeflinger F, Reindl L, Moeller K. Optimal Positioning of Inertial Measurement Units in a Smart Shirt for Determining Respiratory Volume. 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: 38082618 DOI: 10.1109/embc40787.2023.10340473] [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
Tidal volume can be estimated using the surface motions of the upper body induced by respiration. However, the precision and instrumentation of such estimation must be improved to allow widespread application. In this study, respiration induced changes in parameters that can be recorded with inertial measurement units are examined to determine tidal volumes. Based on the data of an optical motion capture system, the optimal positions of inertial measurement units (IMU) in a smart shirt for sets of 4, 5 or 6 sensors were determined. The errors observed indicate the potential to determine tidal volumes using IMUs in a smart shirt.Clinical Relevance- The measurement of respiratory volumes via a low-cost and unobtrusive smart shirt would be a major advance in clinical diagnostics. In particular, conventional methods are expensive, and uncomfortable for conscious patients if measurement is desired over an extended period. A smart-shirt based on inertial sensors would allow a comfortable measurement and could be used in many clinical scenarios - from sleep apnoea monitoring to homecare and respiratory monitoring of comatose patients.
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Romagnoli S, Ripanti F, Morettini M, Burattini L, Sbrollini A. Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063350. [PMID: 36992060 PMCID: PMC10055735 DOI: 10.3390/s23063350] [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: 02/27/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 05/31/2023]
Abstract
Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.
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Neri L, Oberdier MT, Augello A, Suzuki M, Tumarkin E, Jaipalli S, Geminiani GA, Halperin HR, Borghi C. Algorithm for Mobile Platform-Based Real-Time QRS Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:1625. [PMID: 36772665 PMCID: PMC9920820 DOI: 10.3390/s23031625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/17/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan-Tompkins (AMPT), which is a simplified version of the well-established Pan-Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan-Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5-20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Masahito Suzuki
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
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Laufer B, Hoeflinger F, Docherty PD, Jalal NA, Krueger-Ziolek S, Rupitsch SJ, Reindl L, Moeller K. Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals. SENSORS (BASEL, SWITZERLAND) 2023; 23:1278. [PMID: 36772318 PMCID: PMC9920533 DOI: 10.3390/s23031278] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/12/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.
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Affiliation(s)
- Bernhard Laufer
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Fabian Hoeflinger
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Paul D. Docherty
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
| | - Nour Aldeen Jalal
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04109 Leipzig, Germany
| | - Sabine Krueger-Ziolek
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Stefan J. Rupitsch
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Leonhard Reindl
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
| | - Knut Moeller
- Institute of Technical Medicine (ITeM), Furtwangen University, 78054 Villingen-Schwenningen, Germany
- Department of Microsystems Engineering, University of Freiburg, 79085 Freiburg, Germany
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
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Kreyenschulte T, Bohnet-Joschko S. [Patients' Use of Digital Innovations in the Care Process: A Scoping Review]. DAS GESUNDHEITSWESEN 2023; 85:48-57. [PMID: 35654402 DOI: 10.1055/a-1791-0689] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND Digital innovations in healthcare continue to be extensively researched and publicly discussed. The research perspective is often indication-specific or process-oriented and focuses on an application by health professionals in care settings. From the patient's perspective, there are additional digital innovations and opportunities for use that take place privately in addition to sectoral care services. AIM The aim of this scoping review was to map digital innovations currently available for patients and their possible applications in the care process by exploring the following question: Which digital innovations are currently available for patients in health care? MATERIAL AND METHODS A systematic literature search in four databases helped identify 44 international publications as relevant for our analysis. They were categorized and analyzed according to the types of digital innovations, their use by patients, and their location within the care process. In addition, the intentions whereby digital innovations can be applied were discussed. RESULTS We found that current research was focused on patient-applied digital innovations in the therapeutic field, and a broad application spectrum of interfaces for digital care was emerging. These included apps, smart devices, teleconsultation, patient portals, games, implants, robotics, intelligent information and communication systems, and ambient assisted living environments. CONCLUSION Many digitally supported health applications are designed to be used exclusively by patients themselves, or are performed in only partial interaction with providers. In this respect, the active participation and personal responsibility of patients in the treatment process could be strengthened with the help of digital innovations.
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Affiliation(s)
- Thea Kreyenschulte
- Lehrstuhl für Management und Innovation im Gesundheitswesen, Universität Witten/Herdecke, Witten, Germany
| | - Sabine Bohnet-Joschko
- Lehrstuhl für Management und Innovation im Gesundheitswesen, Universität Witten/Herdecke, Witten, Germany
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Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:healthcare10101940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
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Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
- Correspondence: (A.A.I.); or (A.O.B.)
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
- Correspondence: (A.A.I.); or (A.O.B.)
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
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Abstract
PURPOSE OF REVIEW Technology is being increasingly implemented in the fields of cardiac arrest and cardiopulmonary resuscitation. In this review, we describe how recent technological advances have been implemented in the chain of survival and their impact on outcomes after cardiac arrest. Breakthrough technologies that are likely to make an impact in the future are also presented. RECENT FINDINGS Technology is present in every link of the chain of survival, from prediction, prevention, and rapid recognition of cardiac arrest to early cardiopulmonary resuscitation and defibrillation. Mobile phone systems to notify citizen first responders of nearby out-of-hospital cardiac arrest have been implemented in numerous countries with improvement in bystanders' interventions and outcomes. Drones delivering automated external defibrillators and artificial intelligence to support the dispatcher in recognising cardiac arrest are already being used in real-life out-of-hospital cardiac arrest. Wearables, smart speakers, surveillance cameras, and artificial intelligence technologies are being developed and studied to prevent and recognize out-of-hospital and in-hospital cardiac arrest. SUMMARY This review highlights the importance of technology applied to every single step of the chain of survival to improve outcomes in cardiac arrest. Further research is needed to understand the best role of different technologies in the chain of survival and how these may ultimately improve outcomes.
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Affiliation(s)
- Tommaso Scquizzato
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan
| | - Lorenzo Gamberini
- Department of Anaesthesia and Intensive Care and EMS, Maggiore Hospital Bologna, Bologna, Italy
| | - Federico Semeraro
- Department of Anaesthesia and Intensive Care and EMS, Maggiore Hospital Bologna, Bologna, Italy
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Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates. SENSORS 2022; 22:s22041428. [PMID: 35214329 PMCID: PMC8877143 DOI: 10.3390/s22041428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022]
Abstract
The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.
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Khundaqji H, Hing W, Furness J, Climstein M. Wearable technology to inform the prediction and diagnosis of cardiorespiratory events: a scoping review. PeerJ 2021; 9:e12598. [PMID: 35036129 PMCID: PMC8710054 DOI: 10.7717/peerj.12598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/15/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The need for health systems that allow for continuous monitoring and early adverse event detection in individuals outside of the acute care setting has been highlighted by the global rise in chronic cardiorespiratory diseases and the recent COVID-19 pandemic. Currently, it is unclear what type of evidence exists concerning the use of physiological data collected from commercially available wrist and textile wearables to assist in clinical decision making. The aim of this review was therefore to systematically map and summarize the scientific literature surrounding the use of these wearables in clinical decision making as well as identify knowledge gaps to inform further research. METHODOLOGY Six electronic bibliographic databases were systematically searched (Ovid MEDLINE, EMBASE, CINAHL, PubMed, Scopus, and SportsDiscus). Publications from database inception to May 6, 2020 were reviewed for inclusion. Non-indexed literature relevant to this review was also searched systematically. Results were then collated, summarized and reported. RESULTS A total of 107 citations were retrieved and assessed for eligibility with 31 citations included in the final analysis. A review of the 31 papers revealed three major study designs which included (1) observational studies (n = 19), (2) case control series and reports (n = 8), and (3) reviews (n = 2). All papers examined the use of wearable monitoring devices for clinical decisions in the cardiovascular domain, with cardiac arrhythmias being the most studied. When compared to electrocardiogram (ECG) the performance of the wearables in facilitating clinical decisions varied depending upon the type of wearable, user's activity levels and setting in which they were employed. Observational studies collecting data in the inpatient and outpatient settings were equally represented. Eight case control series and reports were identified which reported on the use of wrist wearables in patients presenting to an emergency department or clinic to aid in the clinical diagnosis of a cardiovascular event. Two narrative reviews were identified which examined the impact of wearable devices in monitoring cardiovascular disease as well as potential challenges they may pose in the future. CONCLUSIONS To date, studies employing wearables to facilitate clinical decisions have largely focused upon the cardiovascular domain. Despite the ability of some wearables to collect physiological data accurately, there remains a need for a specialist physician to retrospectively review the raw data to make a definitive diagnosis. Analysis of the results has also highlighted gaps in the literature such as the absence of studies employing wearables to facilitate clinical decisions in the respiratory domain. The disproportionate study of wearables in atrial fibrillation detection in comparison to other cardiac arrhythmias and conditions, as well as the lack of diversity in the sample populations used prevents the generalizability of results.
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Affiliation(s)
- Hamzeh Khundaqji
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Wayne Hing
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - James Furness
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Mike Climstein
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, New South Wales, Australia
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Scquizzato T, Semeraro F. No more unwitnessed out-of-hospital cardiac arrests in the future thanks to technology. Resuscitation 2021; 170:79-81. [PMID: 34822935 DOI: 10.1016/j.resuscitation.2021.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 11/13/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Tommaso Scquizzato
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Semeraro
- Department of Anaesthesia, Intensive Care and Emergency Medical Services, Ospedale Maggiore, Bologna, Italy.
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Spicher N, Klingenberg A, Purrucker V, Deserno TM. Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1735-1739. [PMID: 34891622 DOI: 10.1109/embc46164.2021.9630875] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Fifth-generation (5G) cellular networks promise higher data rates, lower latency, and large numbers of inter-connected devices. Thereby, 5G will provide important steps towards unlocking the full potential of the Internet of Things (IoT). In this work, we propose a lightweight IoT platform for continuous vital sign analysis. Electrocardiography (ECG) is acquired via textile sensors and continuously sent from a smartphone to an edge device using cellular networks. The edge device applies a state-of-the art deep learning model for providing a binary end-to-end classification if a myocardial infarction is at hand. Using this infrastructure, experiments with four volunteers were conducted. We compare 3rd, 4th-, and 5th-generation cellular networks (release 15) with respect to transmission latency, data corruption, and duration of machine learning inference. The best performance is achieved using 5G showing an average transmission latency of 110ms and data corruption in 0.07% of ECG samples. Deep learning inference took approximately 170ms. In conclusion, 5G cellular networks in combination with edge devices are a suitable infrastructure for continuous vital sign analysis using deep learning models. Future 5G releases will introduce multi-access edge computing (MEC) as a paradigm for bringing edge devices nearer to mobile clients. This will decrease transmission latency and eventually enable automatic emergency alerting in near real-time.
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Boulos LJ, Mendes A, Delmas A, Chraibi Kaadoud I. An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health. Front Psychiatry 2021; 12:574440. [PMID: 34630171 PMCID: PMC8495427 DOI: 10.3389/fpsyt.2021.574440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.
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Blachowicz T, Ehrmann G, Ehrmann A. Textile-Based Sensors for Biosignal Detection and Monitoring. SENSORS (BASEL, SWITZERLAND) 2021; 21:6042. [PMID: 34577254 PMCID: PMC8470234 DOI: 10.3390/s21186042] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/16/2021] [Accepted: 09/07/2021] [Indexed: 02/06/2023]
Abstract
Biosignals often have to be detected in sports or for medical reasons. Typical biosignals are pulse and ECG (electrocardiogram), breathing, blood pressure, skin temperature, oxygen saturation, bioimpedance, etc. Typically, scientists attempt to measure these biosignals noninvasively, i.e., with electrodes or other sensors, detecting electric signals, measuring optical or chemical information. While short-time measurements or monitoring of patients in a hospital can be performed by systems based on common rigid electrodes, usually containing a large amount of wiring, long-term measurements on mobile patients or athletes necessitate other equipment. Here, textile-based sensors and textile-integrated data connections are preferred to avoid skin irritations and other unnecessary limitations of the monitored person. In this review, we give an overview of recent progress in textile-based electrodes for electrical measurements and new developments in textile-based chemical and other sensors for detection and monitoring of biosignals.
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Affiliation(s)
- Tomasz Blachowicz
- Center for Science and Education, Institute of Physics, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Guido Ehrmann
- Virtual Institute of Applied Research on Advanced Materials (VIARAM);
| | - Andrea Ehrmann
- Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences, 33619 Bielefeld, Germany
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Abstract
Smart wearable textiles can sense, react, and adapt themselves to external conditions or stimuli, and they can be divided into active and passive smart wearable textiles, which can work with the human brain for cognition, reasoning, and activating capacity. Wearable technology is among the fastest growing parts of health, entertainment, and education. In the future, the development of wearable electronics will be focused on multifunctional, user-friendly, and user acceptance and comfort features and shall be based on advanced electronic textile systems.
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17
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Spörri J, Stöggl T, Aminian K. Editorial: Health and Performance Assessment in Winter Sports. Front Sports Act Living 2021; 3:628574. [PMID: 33768202 PMCID: PMC7985436 DOI: 10.3389/fspor.2021.628574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/12/2021] [Indexed: 12/11/2022] Open
Affiliation(s)
- Jörg Spörri
- Sports Medical Research Group, Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.,University Centre for Prevention and Sports Medicine, Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Thomas Stöggl
- Department of Sport Science and Kinesiology, University of Salzburg, Hallein, Austria.,Red Bull Athlete Performance Centre, Thalgau, Austria
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Alwashmi MF, Fitzpatrick B, Davis E, Farrell J, Gamble JM, Hawboldt J. Features of a mobile health intervention to manage chronic obstructive pulmonary disease: a qualitative study. Ther Adv Respir Dis 2020; 14:1753466620951044. [PMID: 32894025 PMCID: PMC7479870 DOI: 10.1177/1753466620951044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/07/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The use of mobile health (mHealth) interventions has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes. Further research is needed to determine which mHealth features are required to potentially enhance COPD self-management. AIM The aim of this study was to explore the potential features of an mHealth intervention for COPD management with healthcare providers (HCPs) and patients with COPD. It could inform the development and successful implementation of mHealth interventions for COPD management. METHODS This was a qualitative study. We conducted semi-structured individual interviews with HCPs, including nurses, pharmacists and physicians who work directly with patients with COPD. Interviews were also conducted with a diverse sample of patients with COPD. Interview topics included demographics, mHealth usage, the potential use of medical devices and recommendations for features that would enhance an mHealth intervention for COPD management. RESULTS A total of 40 people, including nurses, physicians and pharmacists, participated. The main recommendations for the proposed mHealth intervention were categorised into two categories: patient interface and HCP interface. The prevalent features suggested for the patient interface include educating patients, collecting baseline data, collecting subjective data, collecting objective data via compatible medical devices, providing a digital action plan, allowing patients to track their progress, enabling family members to access the mHealth intervention, tailoring the features based on the patient's unique needs, reminding patients about critical management tasks and rewarding patients for their positive behaviours. The most common features of the HCP interface include allowing HCPs to track their patients' progress, allowing HCPs to communicate with their patients, educating HCPs and rewarding HCPs. CONCLUSION This study identifies important potential features so that the most effective, efficient and feasible mHealth intervention can be developed to improve the management of COPD.The reviews of this paper are available via the supplemental material section.
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Affiliation(s)
- Meshari F. Alwashmi
- Health Sciences Centre, Memorial University of
Newfoundland, 300 Prince Philip Drive, St John’s, NL A1B 3V6, Canada
| | | | - Erin Davis
- Memorial University of Newfoundland, St John’s,
NL, Canada
| | - Jamie Farrell
- Memorial University of Newfoundland, St John’s,
NL, Canada
| | - John-Michael Gamble
- School of Pharmacy, Faculty of Science,
University of Waterloo, Waterloo, ON, Canada
| | - John Hawboldt
- Memorial University of Newfoundland, St John’s,
NL, Canada
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