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Hong J, Seong D, Kang D, Kim H, Jang JH, Jeon M, Kim J. Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor. APL Bioeng 2024; 8:026113. [PMID: 38799376 PMCID: PMC11126325 DOI: 10.1063/5.0203582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.
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Affiliation(s)
- Juyeon Hong
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Daewoon Seong
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Dongwan Kang
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Hyunmo Kim
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Jeong Hun Jang
- Department of Otolaryngology, School of Medicine, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon 16499, South Korea
| | - Mansik Jeon
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Jeehyun Kim
- School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, South Korea
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Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Pyzik R, Kaur S, Chang H, Helmus D, Sands BE, Charney D, Nadkarni G, Bagiella E, Keefer L, Fayad ZA. Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study. JMIR Ment Health 2024; 11:e55552. [PMID: 38663011 PMCID: PMC11082734 DOI: 10.2196/55552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/06/2024] [Accepted: 02/20/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. OBJECTIVE The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. METHODS To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. RESULTS In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. CONCLUSIONS In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.
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Affiliation(s)
- Robert P Hirten
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kyle Landell
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Micol Zweig
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sparshdeep Kaur
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Helena Chang
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Drew Helmus
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bruce E Sands
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Emilia Bagiella
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Laurie Keefer
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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3
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Vraka A, Zangróniz R, Quesada A, Hornero F, Alcaraz R, Rieta JJ. A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 24:141. [PMID: 38203003 PMCID: PMC10781253 DOI: 10.3390/s24010141] [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: 11/19/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.
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Affiliation(s)
- Aikaterini Vraka
- Biosignals and Minimally Invasive Technologies (BioMIT.org), Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Roberto Zangróniz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - Aurelio Quesada
- Arrhythmia Unit, Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Fernando Hornero
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - José J. Rieta
- Biosignals and Minimally Invasive Technologies (BioMIT.org), Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
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4
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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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5
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Baciu VE, Lambert Cause J, Solé Morillo Á, García-Naranjo JC, Stiens J, da Silva B. Anomaly Detection in Multi-Wavelength Photoplethysmography Using Lightweight Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2023; 23:6947. [PMID: 37571730 PMCID: PMC10422657 DOI: 10.3390/s23156947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/18/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts.
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Affiliation(s)
- Vlad-Eusebiu Baciu
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | - Joan Lambert Cause
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
- Department of Biomedical Engineering, Universidad de Oriente, Santiago de Cuba 90500, Cuba
| | - Ángel Solé Morillo
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | | | - Johan Stiens
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | - Bruno da Silva
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
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6
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Vicente-Samper JM, Tamantini C, Ávila-Navarro E, De La Casa-Lillo MÁ, Zollo L, Sabater-Navarro JM, Cordella F. An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts. BIOSENSORS 2023; 13:718. [PMID: 37504116 PMCID: PMC10377343 DOI: 10.3390/bios13070718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 07/29/2023]
Abstract
The heart rate (HR) is a widely used clinical variable that provides important information on a physical user's state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user's wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user's HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.
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Affiliation(s)
- José María Vicente-Samper
- Neuroengineering Biomedical Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Christian Tamantini
- Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Ernesto Ávila-Navarro
- Department of Materials Science, Optics and Electronic Technology, Miguel Hernández University of Elche, 03202 Elche, Spain
| | | | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - José María Sabater-Navarro
- Neuroengineering Biomedical Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Francesca Cordella
- Unit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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Thakur S, Chao PCP, Tsai CH. Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition. SENSORS (BASEL, SWITZERLAND) 2023; 23:6180. [PMID: 37448029 PMCID: PMC10346997 DOI: 10.3390/s23136180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023]
Abstract
A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects' hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios.
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Affiliation(s)
| | - Paul C.-P. Chao
- Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
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8
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Abdulrahaman LQ. Two-Stage Motion Artifact Reduction Algorithm for rPPG Signals Obtained from Facial Video Recordings. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023:1-9. [PMID: 37361465 PMCID: PMC10088718 DOI: 10.1007/s13369-023-07845-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19.
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9
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Kumar A, Ashdhir A, Komaragiri R, Kumar M. Analysis of photoplethysmogram signal to estimate heart rate during physical activity using fractional fourier transform - A sampling frequency independent and reference signal-less method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107294. [PMID: 36528998 DOI: 10.1016/j.cmpb.2022.107294] [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: 02/07/2022] [Revised: 11/13/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Acquiring accurate and reliable health information using a PPG signal in wearable devices requires suppressing motion artifacts. This paper presents a method based on the Fractional Fourier transform (FrFT) to effectively suppress the motion artifacts in a Photoplethysmogram (PPG) signal for an accurate estimation of heart rate (HR). METHODS By analyzing various PPG signals recorded under various physiological conditions and sampling frequencies, the proposed work determines an optimal value of the fractional order of the proposed FrFT. The proposed FrFT-based algorithm separates the motion artifacts component from the acquired PPG signal. Finally, the HR estimation accuracy during the strong motion artifact-affected windows is improved using a post-processing technique. The efficacy of the proposed method is evaluated by computing the root mean square error (RMSE). RESULTS The performance of the proposed algorithm is compared with methods in recent studies using test and training datasets from the IEEE Signal Processing Cup (SPC). The proposed method provides the mean absolute error of 1.88 beats per minute (BPM) on all twenty-three recordings. CONCLUSIONS The proposed method uses the Fourier method in the fractional domain. A noisy signal is rotated into an intermediate plane between the time and frequency domains to separate the signal from the noise. The algorithm incorporates FrFT analysis to suppress motion artifacts from PPG signals to estimate HR accurately. Further, a post-processing step is used to track the HR for accurate and reliable HR estimation. The proposed FrFT-based algorithm doesn't require additional reference accelerometers or hardware to estimate HR in real-time. The noise and signal separation is optimum for a fractional order (a) value in the vicinity of 0.6. The optimized value of fractional order is constant irrespective of the physical activity and sampling frequency.
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Affiliation(s)
- Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Aryaman Ashdhir
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India.
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
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10
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Alkhoury L, Choi J, Chandran VD, De Carvalho GB, Pal S, Kam M. Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation. SENSORS (BASEL, SWITZERLAND) 2022; 22:9955. [PMID: 36560324 PMCID: PMC9782066 DOI: 10.3390/s22249955] [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: 10/29/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.
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Affiliation(s)
- Ludvik Alkhoury
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - JiWon Choi
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Vishnu D. Chandran
- Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Gabriela B. De Carvalho
- Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Saikat Pal
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
- Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Moshe Kam
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
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Prabha A, Yadav J, Rani A, Singh V. Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103876] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Moscato S, Lo Giudice S, Massaro G, Chiari L. Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155831. [PMID: 35957395 PMCID: PMC9370973 DOI: 10.3390/s22155831] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 06/12/2023]
Abstract
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.
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Affiliation(s)
- Serena Moscato
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”—DEI, University of Bologna, 40136 Bologna, Italy;
| | - Stella Lo Giudice
- School of Engineering (Digital Technology Engineering), Pulsed Academy, Fontys University of Applied Science, 5612 MA Eindhoven, The Netherlands;
| | - Giulia Massaro
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy;
| | - Lorenzo Chiari
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”—DEI, University of Bologna, 40136 Bologna, Italy;
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40136 Bologna, Italy
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13
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Krizea M, Gialelis J, Protopsaltis G, Mountzouris C, Theodorou G. Empowering People with a User-Friendly Wearable Platform for Unobtrusive Monitoring of Vital Physiological Parameters. SENSORS (BASEL, SWITZERLAND) 2022; 22:5226. [PMID: 35890907 PMCID: PMC9317673 DOI: 10.3390/s22145226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/02/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject's wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO2) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.
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Affiliation(s)
- Maria Krizea
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - John Gialelis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - Grigoris Protopsaltis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Christos Mountzouris
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Gerasimos Theodorou
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
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Zhang Y, Zhu H, Liu H, Zheng D, Zhang S, Pan Y. A Wearable Swallowing Recognition System Based on Motion and Dual Photoplethysmography Sensing of Laryngeal Movements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:13-16. [PMID: 36085790 DOI: 10.1109/embc48229.2022.9871902] [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
Swallowing recognition is the leading step in the evaluation of dysphagia which seriously affects people's life. Current medical swallowing monitoring methods require an in-hospital environment and overly rely on professional knowledge of the medical staff. In this study, we developed a wearable swallowing recognition system that consists of an on-neck wearable swallowing sensing device and a data processing module on a host computer. The wearable device collects inertial signals including acceleration and angular velocity, as well as dual photoplethysmography (PPG) signals based on infrared and green light from the neck. A novel processing framework for dual PPG signals is proposed to extract and enhance the laryngeal motion component introduced by swallowing activities in the data processing module. The laryngeal motion component of dual PPG signals together with the preprocessed inertial signals are further used for feature extraction to proceed swallowing recognition based on random forest classifier. We collected data from 32 healthy subjects in the center and side positions on the neck using our system to analyze their swallowing activities. As a result, we achieved a high average area under curve (AUC) of the swallowing recognition by 86.6%. We also find the sensing position has a significant impact on gender-specific swallowing recognition performance, as the center position was better for females (92.9%), while the side position was better for males (87.6%). The results indicate that the proposed system could achieve high integrity and good performance, which is helpful for the future swallowing research.
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15
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Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market. SENSORS 2022; 22:s22072731. [PMID: 35408345 PMCID: PMC9002880 DOI: 10.3390/s22072731] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022]
Abstract
Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.
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16
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Merschel S, Reinhardt L. Analyzability of Photoplethysmographic Smartwatch Data by the Preventicus Heartbeats Algorithm During Everyday Life: Feasibility Study. JMIR Form Res 2022; 6:e29479. [PMID: 35343902 PMCID: PMC9002588 DOI: 10.2196/29479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 12/14/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts. OBJECTIVE The aim of this investigation was to evaluate the analyzability of smartwatch-derived PPG data during everyday life and to determine the relationship between the analyzability of the data and the activity level of the participant. METHODS A total of 41 (19 female and 22 male) adults in good cardiovascular health (aged 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a 3D accelerometer (Cardio Watch 287, Corsano Health BV) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analyzed on a 1-minute basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH). As outcomes, the percentage of analyzable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of the ACC and PAD over the course of one day, the 24-hour period was divided into 8 subintervals comprising 3 hours each. RESULTS Univariate analysis of variance showed a large effect (ηp2> 0.6; P<.001) of time interval (phase) on the ACC and PAD. The PAD ranged between 34% and 100%, with an average of 71.5% for the whole day, which is equivalent to a period of 17.2 hours. Between midnight and 6 AM, the mean values were the highest for the PAD (>94%) and the lowest for the ACC (<6×10-3 m/s2). Regardless of the time of the day, the correlation between the PAD and ACC was strong (r=-0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r2=0.99). CONCLUSIONS This study showed a large relationship between the activity level and the analyzability of smartwatch-derived PPG data. Given the high yield of analyzable data during the nighttime, continuous arrhythmia screening seems particularly effective during sleep phases.
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Affiliation(s)
| | - Lars Reinhardt
- Institute for Applied Training Science, Leipzig, Germany
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17
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Marutani Y, Konda S, Ogasawara I, Yamasaki K, Yokoyama T, Maeshima E, Nakata K. An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity. SENSORS (BASEL, SWITZERLAND) 2022; 22:2577. [PMID: 35408192 PMCID: PMC9003462 DOI: 10.3390/s22072577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/07/2022] [Accepted: 03/25/2022] [Indexed: 11/16/2022]
Abstract
Sportswear-type wearables with integrated inertial sensors and electrocardiogram (ECG) electrodes have been commercially developed. We evaluated the feasibility of using a sportswear-type wearable with integrated inertial sensors and electrocardiogram (ECG) electrodes for evaluating exercise intensity within a controlled laboratory setting. Six male college athletes were asked to wear a sportswear-type wearable while performing a treadmill test that reached up to 20 km/h. The magnitude of the filtered tri-axial acceleration signal, recorded by the inertial sensor, was used to calculate the acceleration index. The R-R intervals of the ECG were used to determine heart rate; the external validity of the heart rate was then evaluated according to oxygen uptake, which is the gold standard for physiological exercise intensity. Single regression analysis between treadmill speed and the acceleration index in each participant showed that the slope of the regression line was significantly greater than zero with a high coefficient of determination (walking, 0.95; jogging, 0.96; running, 0.90). Another single regression analysis between heart rate and oxygen uptake showed that the slope of the regression line was significantly greater than zero, with a high coefficient of determination (0.96). Together, these results indicate that the sportswear-type wearable evaluated in this study is a feasible technology for evaluating physical and physiological exercise intensity across a wide range of physical activities and sport performances.
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Affiliation(s)
- Yoshihiro Marutani
- Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, Kumatori 590-0496, Osaka, Japan; (Y.M.); (E.M.)
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
| | - Shoji Konda
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
- Department of Sports Medical Biomechanics, Osaka University Graduate School of Medicine, Suita 565-0871, Osaka, Japan
| | - Issei Ogasawara
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
- Department of Sports Medical Biomechanics, Osaka University Graduate School of Medicine, Suita 565-0871, Osaka, Japan
| | - Keita Yamasaki
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
| | - Teruki Yokoyama
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
| | - Etsuko Maeshima
- Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, Kumatori 590-0496, Osaka, Japan; (Y.M.); (E.M.)
| | - Ken Nakata
- Department of Health and Sport Sciences, Osaka University Graduate School of Medicine, Toyonaka 560-0043, Osaka, Japan; (S.K.); (I.O.); (K.Y.); (T.Y.)
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Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 05/29/2023]
Abstract
Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Panicos A. Kyriacou
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
| | - Jonathan Mant
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Vaidotas Marozas
- Department of Electronics Engineering and the Biomedical Engineering Institute, Kaunas University of Technology44249KaunasLithuania
| | - Phil Chowienczyk
- Department of Clinical PharmacologyKing’s College LondonLondonSE1 7EHU.K.
| | - Jordi Alastruey
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
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Pandey RK, Chao PCP. A Dual-Channel PPG Readout System With Motion-Tolerant Adaptability for OLED-OPD Sensors. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:36-51. [PMID: 34962876 DOI: 10.1109/tbcas.2021.3138996] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An adaptive PPG (Photoplethysmography) readout system for a dual-channel OLED-OPD flexible sensor is designed and developed with motion artifact (<1Hz) and ambient lighting interference successfully compensated without any additional motion sensors. The compensation is made possible by adopting multi-feedbacks and an additional reference OPD channel to cancel effectively DC drifts. In result, the quality of measured PPG is improved to the level such that long-time, continuous quality monitoring of bio-sign such as heart rate (HR) is possible. The readout is designed with an auto-programmable band-pass trans-impedance amplifier (TIA) of a 100dbΩ gain with a continuous-type DC-current cancellation loop. The rest of the readout consists of a 0.5 Hz low-pass filter, an additional second-order band-pass filter (0.1-10Hz), a difference amplifier, a motion reference channel, an analog multiplexer, a programmable gain amplifier (PGA), a digital control and a programmable DAC-PWM based auto-intensity tuned OLED driver. The readout is fabricated in an area of 9 mm2 via the TSMC 180nm process. The experiment result shows that the developed OLED-OPD readout senses well as small as 1nA current, with a measured dynamic range >90dB (1nA to 100 µA) and input-referred noise of 0.26 nA/√H, with power consumption of 460µW. The DC drift is successfully reduced to 1% of its average. The accuracy for heart rate is 96%.
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Guo Z, Ding C, Hu X, Rudin C. A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables. Physiol Meas 2021; 42. [PMID: 34794126 DOI: 10.1088/1361-6579/ac3b3d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 11/18/2021] [Indexed: 12/21/2022]
Abstract
Objective. Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals.Approach. We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset.Main results. We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734 ± 0.0018, 0.9114 ± 0.0033 and 0.8050 ± 0.0116 respectively. The next best method only achieved 0.8068 ± 0.0014, 0.8446 ± 0.0013 and 0.7247 ± 0.0050.Significance. The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia.
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Affiliation(s)
- Zhicheng Guo
- Department of Computer Science, Duke University, United States of America
| | - Cheng Ding
- Department of Electrical and Computer Engineering, Duke University, United States of America
| | - Xiao Hu
- Department of Electrical and Computer Engineering, Duke University, United States of America.,Division of Health Analytics, School of Nursing, Biomedical Engineering, Pratt School of Engineering, Departments of Neurology, Biostatistics & Bioinformatics, Surgery, School of Medicine, Duke University, United States of America
| | - Cynthia Rudin
- Department of Computer Science, Duke University, United States of America.,Department of Electrical and Computer Engineering, Duke University, United States of America
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21
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Somani SN, Yu KM, Chiu AG, Sykes KJ, Villwock JA. Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review. Otolaryngol Head Neck Surg 2021; 167:620-631. [PMID: 34813407 DOI: 10.1177/01945998211061681] [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: 11/17/2022]
Abstract
OBJECTIVE Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients. DATA SOURCES PubMed. REVIEW METHODS A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review. CONCLUSIONS In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care. IMPLICATIONS FOR PRACTICE Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.
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Affiliation(s)
- Shaan N Somani
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Katherine M Yu
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Alexander G Chiu
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Kevin J Sykes
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jennifer A Villwock
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, USA
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22
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Kechris C, Delopoulos A. RMSSD Estimation From Photoplethysmography and Accelerometer Signals Using a Deep Convolutional Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:228-231. [PMID: 34891278 DOI: 10.1109/embc46164.2021.9629595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Heart Rate Variability is a significant indicator of the Autonomic Neural System's functioning, traditionally evaluated from electrocardiogram recordings. Photoplethysmography sensors, like electrocardiograph devices, track the heart's activity and have been widely popularized by their use in smart watches and fitness trackers. In this study we develop a deep learning based approach which is able to successfully estimate the patient's Root Mean Square of the Successive Differences, a common heart rate variability metric, from lower quality, less expensive photoplethysmography sensors under a wide range of conditions.
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23
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Mao R, Tweardy M, Wegerich SW, Goergen CJ, Wodicka GR, Zhu F. Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5625-5630. [PMID: 34892399 DOI: 10.1109/embc46164.2021.9630595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion Artifact (MA) reduction techniques have been developed and evaluated using either synthetic noisy signals or signals collected during high-intensity activities - both of which are difficult to generalize for real-life scenarios. Therefore, it is valuable to collect realistic PPG signals while performing Activities of Daily Living (ADL) to develop practical signal denoising and analysis methods. In this work, we propose an automatic pseudo clean PPG generation process for reliable PPG signal selection. For each noisy PPG segment, the corresponding pseudo clean PPG reduces the MAs and contains rich temporal details depicting cardiac features. Our experimental results show that 71% of the pseudo clean PPG collected from ADL can be considered as high quality segment where the derived MAE of heart rate and respiration rate are 1.46 BPM and 3.93 BrPM, respectively. Therefore, our proposed method can determine the reliability of the raw noisy PPG by considering quality of the corresponding pseudo clean PPG signal.
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Zanelli S, El Yacoubi MA, Hallab M, Ammi M. Transfer learning of CNN-based signal quality assessment from clinical to non-clinical PPG signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:902-905. [PMID: 34891436 DOI: 10.1109/embc46164.2021.9629640] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Photoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variation inside the micro-circulation. PPG technology is widely used in a variety of clinical and non-clinical devices in order to investigate the cardiovascular system. One example of clinical PPG device is the pulse oxymeter, while non-clinical PPG devices include smartphones and smartwatches. Such a wide diffusion of PPG devices generates plenty of different PPG signals that differ from each other. In fact, intrinsic device characteristics strongly influence PPG waveform. In this paper we investigate transfer learning approaches on a Covolutional Neural Network based quality assessment method in order to generalize our model across different PPG devices. Our results show that our model is able to classify accurately signal quality over different PPG datasets while requiring a small amount of data for fine-tuning.Clinical relevance- A precise detection and extraction of high quality PPG segments could enhance significantly the reliability of the medical analysis based on the signal.
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25
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Neshitov A, Tyapochkin K, Smorodnikova E, Pravdin P. Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment. SENSORS (BASEL, SWITZERLAND) 2021; 21:6798. [PMID: 34696011 PMCID: PMC8538953 DOI: 10.3390/s21206798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/01/2021] [Accepted: 10/06/2021] [Indexed: 11/20/2022]
Abstract
Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person's movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals' self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
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Affiliation(s)
- Alexander Neshitov
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA 94063, USA; (E.S.); (P.P.)
| | - Konstantin Tyapochkin
- Welltory Inc., 541 Jefferson, Suite 100, Redwood City, CA 94063, USA; (E.S.); (P.P.)
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Review of Wearable Devices and Data Collection Considerations for Connected Health. SENSORS 2021; 21:s21165589. [PMID: 34451032 PMCID: PMC8402237 DOI: 10.3390/s21165589] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/22/2021] [Accepted: 08/02/2021] [Indexed: 12/16/2022]
Abstract
Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.
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27
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Babusiak B, Hajducik A, Medvecky S, Lukac M, Klarak J. Design of Smart Steering Wheel for Unobtrusive Health and Drowsiness Monitoring. SENSORS 2021; 21:s21165285. [PMID: 34450727 PMCID: PMC8399225 DOI: 10.3390/s21165285] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 12/26/2022]
Abstract
This article describes the design of a smart steering wheel intended for use in unobtrusive health and drowsiness monitoring. The aging population, cardiovascular disease, personalized medicine, and driver fatigue were significant motivations for developing a monitoring platform in cars because people spent much time in cars. The purpose was to create a unique, comprehensive monitoring system for the driver. The crucial parameters in health or drowsiness monitoring, such as heart rate, heart rate variability, and blood oxygenation, are measured by an electrocardiograph and oximeter integrated into the steering wheel. In addition, an inertial unit was integrated into the steering wheel to record and analyze the movement patterns performed by the driver while driving. The developed steering wheel was tested under laboratory and real-life conditions. The measured signals were verified by commercial devices to confirm data correctness and accuracy. The resulting signals show the applicability of the developed platform in further detecting specific cardiovascular diseases (especially atrial fibrillation) and drowsiness.
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Affiliation(s)
- Branko Babusiak
- Department of Electromagnetic and Biomedical Engineering, University of Zilina, 01026 Zilina, Slovakia
- Correspondence:
| | - Adrian Hajducik
- Department of Design and Machine Elements, University of Zilina, 01026 Zilina, Slovakia; (A.H.); (M.L.)
| | - Stefan Medvecky
- Institute of Competitiveness and Innovation, University of Zilina, 01026 Zilina, Slovakia;
| | - Michal Lukac
- Department of Design and Machine Elements, University of Zilina, 01026 Zilina, Slovakia; (A.H.); (M.L.)
| | - Jaromir Klarak
- Department of Automated Production Systems, University of Zilina, 01026 Zilina, Slovakia;
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Wan EY, Ghanbari H, Akoum N, Itzhak Attia Z, Asirvatham SJ, Chung EH, Dagher L, Al-Khatib SM, Stuart Mendenhall G, McManus DD, Pathak RK, Passman RS, Peters NS, Schwartzman DS, Svennberg E, Tarakji KG, Turakhia MP, Trela A, Yarmohammadi H, Marrouche NF. HRS White Paper on Clinical Utilization of Digital Health Technology. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 2:196-211. [PMID: 35265910 PMCID: PMC8890053 DOI: 10.1016/j.cvdhj.2021.07.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.
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Affiliation(s)
- Elaine Y. Wan
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | | | | | | | | | | | - Lilas Dagher
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, New Orleans, LA, USA
| | | | | | | | - Rajeev K. Pathak
- Cardiac Electrophysiology Unit, Department of Cardiology, Canberra Hospital and Health Services, Australian National University, Canberra, Australia
| | - Rod S. Passman
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | | | - Emma Svennberg
- Karolinska Institutet, Department of Medicine Huddinge, Karolinska University Hospital, Stockholm, Sweden
| | - Khaldoun G. Tarakji
- Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Mintu P. Turakhia
- Department of Medicine, Stanford University, Stanford, California; Veterans Affairs Palo Alto Health Care System, Palo Alto, California, and Center for Digital Health, Stanford, CA, USA
| | - Anthony Trela
- Lucile Packard Children’s Hospital, Pediatric Cardiology, Palo Alto, CA, USA
| | - Hirad Yarmohammadi
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, New York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - Nassir F. Marrouche
- Tulane Research Innovation for Arrhythmia Discoveries (TRIAD), Heart and Vascular Institute, Tulane University School of Medicine, New Orleans, LA, USA
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Evaluation of biases in remote photoplethysmography methods. NPJ Digit Med 2021; 4:91. [PMID: 34083724 PMCID: PMC8175478 DOI: 10.1038/s41746-021-00462-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/03/2021] [Indexed: 12/21/2022] Open
Abstract
This work investigates the estimation biases of remote photoplethysmography (rPPG) methods for pulse rate measurement across diverse demographics. Advances in photoplethysmography (PPG) and rPPG methods have enabled the development of contact and noncontact approaches for continuous monitoring and collection of patient health data. The contagious nature of viruses such as COVID-19 warrants noncontact methods for physiological signal estimation. However, these approaches are subject to estimation biases due to variations in environmental conditions and subject demographics. The performance of contact-based wearable sensors has been evaluated, using off-the-shelf devices across demographics. However, the measurement uncertainty of rPPG methods that estimate pulse rate has not been sufficiently tested across diverse demographic populations or environments. Quantifying the efficacy of rPPG methods in real-world conditions is critical in determining their potential viability as health monitoring solutions. Currently, publicly available face datasets accompanied by physiological measurements are typically captured in controlled laboratory settings, lacking diversity in subject skin tones, age, and cultural artifacts (e.g, bindi worn by Indian women). In this study, we collect pulse rate and facial video data from human subjects in India and Sierra Leone, in order to quantify the uncertainty in noncontact pulse rate estimation methods. The video data are used to estimate pulse rate using state-of-the-art rPPG camera-based methods, and compared against ground truth measurements captured using an FDA-approved contact-based pulse rate measurement device. Our study reveals that rPPG methods exhibit similar biases when compared with a contact-based device across demographic groups and environmental conditions. The mean difference between pulse rates measured by rPPG methods and the ground truth is found to be ~2% (1 beats per minute (b.p.m.)), signifying agreement of rPPG methods with the ground truth. We also find that rPPG methods show pulse rate variability of ~15% (11 b.p.m.), as compared to the ground truth. We investigate factors impacting rPPG methods and discuss solutions aimed at mitigating variance.
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30
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Gualniera L, Singh J, Fiori F, Santosh P. Emotional Behavioural and Autonomic Dysregulation (EBAD) in Rett Syndrome - EDA and HRV monitoring using wearable sensor technology. J Psychiatr Res 2021; 138:186-193. [PMID: 33862302 DOI: 10.1016/j.jpsychires.2021.03.052] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 03/01/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Rett syndrome (RTT) is a severe genetic neurodevelopmental disorder. Emotional, Behavioural and Autonomic Dysregulation (EBAD), is frequent in RTT and is associated with multiple impairments. There are major challenges in the clinical assessment of emotions, behaviours, and autonomic function in RTT patients that limit the management of symptoms. METHODS Web-based technology (HealthTracker™) to measure the phenotype, and non-invasive, wearable sensor technology to evaluate autonomic function through Electrodermal Activity (EDA) and Heart Rate Variability (HRV) in 10 RTT patients was used, and treatment response of EBAD symptoms was monitored after different pharmacological treatments. RESULTS and discussion: 4 patients received buspirone, 2 sertraline, 1 gabapentin and 3 were not started on medications. Buspirone normalized the EDA in 3 patients with associated improvement in EBAD, whilst another patient only improved marginally. Both patients treated with sertraline (including one with normal EDA) significantly improved symptomatically. The patients on unchanged regimens showed no change in symptoms and autonomic function. Within 24 h of our assessment, one patient required intensive inpatient care due to septicaemia - this patient had been on gabapentin and showed a sharp and sustained EDA increase without obvious worsening of emotional and behavioural symptoms. Unlike the EDA, the analyses of HRV metrics did not reveal patterns that were associated with clinical outcomes. Our findings suggest a reasonable association of EDA normalization and symptomatic improvement in RTT subjects with EBAD treated with buspirone and point out its potential application as a measure of dysautonomia in RTT. Very high and sustained EDA levels may be a biomarker for concurrent serious physical illness in RTT.
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Affiliation(s)
- Ludovica Gualniera
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, UK; Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jatinder Singh
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, UK; Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico Fiori
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, UK; Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paramala Santosh
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, UK; Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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Groenendaal W, Lee S, van Hoof C. Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions. JMIR BIOMEDICAL ENGINEERING 2021; 6:e22911. [PMID: 38907374 PMCID: PMC11041432 DOI: 10.2196/22911] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 03/01/2021] [Accepted: 04/06/2021] [Indexed: 01/20/2023] Open
Abstract
Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.
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Affiliation(s)
| | - Seulki Lee
- Imec the Netherlands / Holst Centre, Eindhoven, Netherlands
| | - Chris van Hoof
- Imec, Leuven, Belgium
- One Planet Research Center, Wageningen, Netherlands
- Department of Engineering Science, KU Leuven, Leuven, Belgium
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Fine J, Branan KL, Rodriguez AJ, Boonya-ananta T, Ajmal, Ramella-Roman JC, McShane MJ, Coté GL. Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring. BIOSENSORS 2021; 11:126. [PMID: 33923469 PMCID: PMC8073123 DOI: 10.3390/bios11040126] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/30/2021] [Accepted: 04/09/2021] [Indexed: 12/14/2022]
Abstract
Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring.
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Affiliation(s)
- Jesse Fine
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; (J.F.); (K.L.B.)
| | - Kimberly L. Branan
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; (J.F.); (K.L.B.)
| | - Andres J. Rodriguez
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA; (A.J.R.); (T.B.-a.); (A.); (J.C.R.-R.)
| | - Tananant Boonya-ananta
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA; (A.J.R.); (T.B.-a.); (A.); (J.C.R.-R.)
| | - Ajmal
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA; (A.J.R.); (T.B.-a.); (A.); (J.C.R.-R.)
| | - Jessica C. Ramella-Roman
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA; (A.J.R.); (T.B.-a.); (A.); (J.C.R.-R.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA
| | - Michael J. McShane
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; (J.F.); (K.L.B.)
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA
- Center for Remote Health Technologies and Systems, Texas A&M Engineering Experimentation Station, Texas A&M University, College Station, TX 77843, USA
| | - Gerard L. Coté
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; (J.F.); (K.L.B.)
- Center for Remote Health Technologies and Systems, Texas A&M Engineering Experimentation Station, Texas A&M University, College Station, TX 77843, USA
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Kuwalek P, Burlaga B, Jesko W, Konieczka P. Research on methods for detecting respiratory rate from photoplethysmographic signal. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102483] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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34
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Reply: Matters Arising 'Investigating sources of inaccuracy in wearable optical heart rate sensors'. NPJ Digit Med 2021; 4:39. [PMID: 33637842 PMCID: PMC7910441 DOI: 10.1038/s41746-021-00409-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 01/26/2021] [Indexed: 11/19/2022] Open
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35
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De Pinho Ferreira N, Gehin C, Massot B. A Review of Methods for Non-Invasive Heart Rate Measurement on Wrist. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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36
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Mühlen JM, Stang J, Lykke Skovgaard E, Judice PB, Molina-Garcia P, Johnston W, Sardinha LB, Ortega FB, Caulfield B, Bloch W, Cheng S, Ekelund U, Brønd JC, Grøntved A, Schumann M. Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network. Br J Sports Med 2021; 55:767-779. [PMID: 33397674 PMCID: PMC8273688 DOI: 10.1136/bjsports-2020-103148] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2020] [Indexed: 01/06/2023]
Abstract
Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.
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Affiliation(s)
- Jan M Mühlen
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University Cologne, Cologne, Germany
| | - Julie Stang
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
| | - Esben Lykke Skovgaard
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense, Denmark
| | - Pedro B Judice
- Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisboa, Portugal.,CIDEFES - Centro de Investigação em Desporto, Educação Física e Exercício e Saúde, Universidade Lusófona, Lisboa, Portugal
| | - Pablo Molina-Garcia
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada, Granada, Spain
| | - William Johnston
- SFI Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Luís B Sardinha
- Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisboa, Cruz-Quebrada Dafundo, Portugal
| | - Francisco B Ortega
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical and Sports Education, Faculty of Sport Sciences, University of Granada, Granada, Spain.,Department of Biosciences and Nutrition, Karolinska Institute, Stockholm, Sweden
| | - Brian Caulfield
- SFI Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Wilhelm Bloch
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University Cologne, Cologne, Germany
| | - Sulin Cheng
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University Cologne, Cologne, Germany.,Exercise Translational Medicine Centre, the Key Laboratory of Systems Biomedicine, Ministry of Education, and Exercise, Health and Technology Centre, Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China
| | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
| | - Jan Christian Brønd
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense, Denmark
| | - Anders Grøntved
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense, Denmark
| | - Moritz Schumann
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University Cologne, Cologne, Germany .,Exercise Translational Medicine Centre, the Key Laboratory of Systems Biomedicine, Ministry of Education, and Exercise, Health and Technology Centre, Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China
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37
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Ferreira NDP, Gehin C, Massot B. Optical flow sensor as a reference for reduction of motion artefacts in photoplethysmographic measurements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4421-4424. [PMID: 33018975 DOI: 10.1109/embc44109.2020.9175787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Methods commonly used for reduction of motion artefacts in photoplethysmography employ accelerometry as a reference for adaptive filtering and signal processing. In this paper, we propose the use of an optical flow sensor to measure the relative displacement between a photoplethysmographic sensor and the measurement site. In order to evaluate the performances of this novel method, a wrist-worn device that enables simultaneous acquisition of physiological information and relative motion has been developed. The optical flow sensor provides a two-dimensional information source correlated with artefacts contained in the cardiac frequency band. Preliminary results show a clear correlation between motion recorded by the sensor and artefacts contained in the photoplethysmographic signal. In association with adaptive filtering, the proposed technique shows efficient reduction of motion artefacts during physical activity.
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38
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Sheridan DC, Dehart R, Lin A, Sabbaj M, Baker SD. Heart Rate Variability Analysis: How Much Artifact Can We Remove? Psychiatry Investig 2020; 17:960-965. [PMID: 33017533 PMCID: PMC7538246 DOI: 10.30773/pi.2020.0168] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/26/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology. METHODS This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject's HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered. RESULTS Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed. CONCLUSION Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.
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Affiliation(s)
- David C Sheridan
- Department of Emergency Medicine, Oregon Health & Science University, Portland, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Portland, USA
| | - Ryan Dehart
- Department of Emergency Medicine, Oregon Health & Science University, Portland, USA
| | - Amber Lin
- Department of Emergency Medicine, Oregon Health & Science University, Portland, USA.,Center of Policy and Research in Emergency Medicine, Oregon Health & Science University, Portland, USA
| | - Michael Sabbaj
- Department of Emergency Medicine, Oregon Health & Science University, Portland, USA
| | - Steven D Baker
- Department of Emergency Medicine, Oregon Health & Science University, Portland, USA.,AlphaBravo Connectivity, LLC, Beaverton, USA
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39
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El Atrache R, Tamilia E, Mohammadpour Touserkani F, Hammond S, Papadelis C, Kapur K, Jackson M, Bucciarelli B, Tsuboyama M, Sarkis RA, Loddenkemper T. Photoplethysmography: A measure for the function of the autonomic nervous system in focal impaired awareness seizures. Epilepsia 2020; 61:1617-1626. [DOI: 10.1111/epi.16621] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 06/29/2020] [Accepted: 06/29/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Rima El Atrache
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Eleonora Tamilia
- Children's Brain Dynamics Division of Newborn Medicine Department of Medicine Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Fetal‐Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston Massachusetts USA
| | - Fatemeh Mohammadpour Touserkani
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Department of Neurology SUNY Downstate Medical Center Brooklyn New York USA
| | - Sarah Hammond
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Christos Papadelis
- Children's Brain Dynamics Division of Newborn Medicine Department of Medicine Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Jane and John Justin Neurosciences Center Cook Children's Health Care System Fort Worth Texas USA
- Department of Bioengineering University of Texas at Arlington Arlington Texas USA
| | - Kush Kapur
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Bethany Bucciarelli
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Melissa Tsuboyama
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Rani A. Sarkis
- Department of Neurology Brigham and Women's HospitalHarvard Medical School Boston Massachusetts USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
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40
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Tipparaju VV, Xian X, Bridgeman D, Wang D, Tsow F, Forzani E, Tao N. Reliable Breathing Tracking with Wearable Mask Device. IEEE SENSORS JOURNAL 2020; 20:5510-5518. [PMID: 33746622 PMCID: PMC7977629 DOI: 10.1109/jsen.2020.2969635] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Breathing tracking is critical for the assessment of lung functions, exercise physiologies, and energy expenditure. Conventional methods require using a face mask or mouthpiece that is connected to a stationary equipment through a tube, restricting the location, movement, or even the posture. To obtain accurate breathing physiology parameters that represent the true state of the patient during different scenarios, a wearable technology that has less intervention to patient's activities in free-living conditions is highly preferred. Here, we propose a miniaturized, reliable, and wide-dynamic ranged flow sensing technology that is immune to orientation, movement, and noise. As far as we know, this is the first work of introducing a fully integrated mask device focusing on breath tracking in free-living conditions. There are two key challenges for achieving this goal: miniaturized flow sensing and motion-induced artifacts elimination. To address these challenges, we come up with two technical innovations: 1) in hardware wise, we have designed an integrated flow sensing technique based on differential pressure Pneumotach approach and motion sensing; 2) in software wise, we have developed comprehensive algorithms based baseline tracking and orientation and motion compensation. The effectiveness of the proposed technology has been proven by the experiments. Experimental results from simulator and real breath conditions show high correlation (R2 = 0.9994 and 0.9964 respectively) and mean error within 2.5% for Minute Volume (VE), when compared to values computed from reference methods. These results show that the proposed method is accurate and reliable to track the key breath parameters in free-living conditions.
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Affiliation(s)
- Vishal Varan Tipparaju
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Xiaojun Xian
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Devon Bridgeman
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Di Wang
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Francis Tsow
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Erica Forzani
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
| | - Nongjian Tao
- Center for Bioelectronics & Biosensors, the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA
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41
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Kumar S, Buckley JL, Barton J, Pigeon M, Newberry R, Rodencal M, Hajzeraj A, Hannon T, Rogers K, Casey D, O’Sullivan D, O’Flynn B. A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications. SENSORS 2020; 20:s20061675. [PMID: 32192204 PMCID: PMC7147171 DOI: 10.3390/s20061675] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 11/16/2022]
Abstract
A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.
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Affiliation(s)
- Sanjeev Kumar
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
- Correspondence: ; Tel.: +353-212-346-109
| | - John L. Buckley
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - John Barton
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Melusine Pigeon
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Robert Newberry
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Matthew Rodencal
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Adhurim Hajzeraj
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Tim Hannon
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Ken Rogers
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Declan Casey
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Donal O’Sullivan
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
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42
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Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. SENSORS 2020; 20:s20051493. [PMID: 32182772 PMCID: PMC7085621 DOI: 10.3390/s20051493] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/02/2020] [Accepted: 03/07/2020] [Indexed: 11/17/2022]
Abstract
Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.
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43
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Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med 2020; 3:18. [PMID: 32047863 PMCID: PMC7010823 DOI: 10.1038/s41746-020-0226-6] [Citation(s) in RCA: 240] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 01/17/2020] [Indexed: 11/15/2022] Open
Abstract
As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
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Affiliation(s)
- Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC USA
| | | | - Warren A. Kibbe
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC USA
| | - Jessilyn P. Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC USA
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC USA
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44
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Song S, Konijnenburg M, van Wegberg R, Xu J, Ha H, Sijbers W, Stanzione S, Biswas D, Breeschoten A, Vis P, van Liempd C, van Hoof C, van Helleputte N. A 769 μW Battery-Powered Single-Chip SoC With BLE for Multi-Modal Vital Sign Monitoring Health Patches. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:1506-1517. [PMID: 31581099 DOI: 10.1109/tbcas.2019.2945114] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
An all-in-one battery powered low-power SoC for measuring multiple vital signs with wearables is proposed. All functionality needed in a typical wearable use case scenario, including dedicated readouts, power management circuitry, digital signal processing and wireless communication (BLE) is integrated in a single die. This high level of integration allows an unprecedented level of miniaturization leading to smaller component count which reduces cost and improves comfort and signal integrity. The SoC includes an ECG, Bio-Impedance and a fully differential PPG readout and can interface with external sensors (like an IMU). In a typical application scenario where all sensor readouts are enabled and key features (like heart rate) are calculated on the chip and streamed over the radio, the SoC consumes only 769 μW from the regulated 1.2 V supply.
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