Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals

A key access point to the functioning of the Autonomic Nervous System is the investigation of peripheral signals. Wearable Devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. But achievable data quality can be lower, subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and Electrodermal Activity signals is validated with a standard set of Signal Quality Indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of 6 different physiological measures collected from 18 subjects with WDs. This study indicates the need of validating the use of WD in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducibility of results.

[1]  M. Nock,et al.  Using Wearable Physiological Monitors With Suicidal Adolescent Inpatients: Feasibility and Acceptability Study , 2019, JMIR mHealth and uHealth.

[2]  R. Levenson.  The Autonomic Nervous System and Emotion , 2014 .

[3]  Anne-Marie Brouwer,et al.  A Comparison between Laboratory and Wearable Sensors in the Context of Physiological Synchrony , 2020, ICMI.

[4]  M. Bornstein,et al.  Strangers, Friends, and Lovers Show Different Physiological Synchrony in Different Emotional States , 2019, Behavioral sciences.

[5]  Mohamed Elgendi,et al.  Optimal Signal Quality Index for Photoplethysmogram Signals , 2016, Bioengineering.

[6]  Kristof Van Laerhoven,et al.  Wearable-Based Affect Recognition—A Review , 2019, Sensors.

[7]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[8]  R G Mark,et al.  Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter , 2008, Physiological measurement.

[9]  Mohammad Soleymani,et al.  A Multimodal Database for Affect Recognition and Implicit Tagging , 2012, IEEE Transactions on Affective Computing.

[10]  Maria Lindén,et al.  A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption , 2017, Journal of Medical Systems.

[11]  Ming-Zher Poh,et al.  Wearable sensors: Opportunities and challenges for low-cost health care , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[12]  Y. Koumpouros,et al.  Wearables and mobile technologies in Autism Spectrum Disorder interventions: A systematic literature review , 2019, Research in Autism Spectrum Disorders.

[13]  Anne-Marie Brouwer,et al.  A Usability Study of Physiological Measurement in School Using Wearable Sensors , 2020, Sensors.

[14]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Cesare Furlanello,et al.  pyphysio: A physiological signal processing library for data science approaches in physiology , 2019, SoftwareX.

[16]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[17]  The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient‐ and device‐specific factors , 2020, Journal of sleep research.

[18]  Sylvia D. Kreibig,et al.  Autonomic nervous system activity in emotion: A review , 2010, Biological Psychology.

[19]  E. Gordon,et al.  Separating individual skin conductance responses in a short interstimulus-interval paradigm , 2005, Journal of Neuroscience Methods.

[20]  H-S Philip Wong,et al.  Continuous wireless pressure monitoring and mapping with ultra-small passive sensors for health monitoring and critical care , 2014, Nature Communications.

[21]  Ki H. Chon,et al.  Improving Pulse Rate Measurements during Random Motion Using a Wearable Multichannel Reflectance Photoplethysmograph , 2016, Sensors.

[22]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[23]  V. Somers,et al.  Heart Rate Variability: , 2003, Journal of cardiovascular electrophysiology.

[24]  Matthew S. Goodwin,et al.  Atypical Emotional Electrodermal Activity in Toddlers with Autism Spectrum Disorder , 2020, Autism research : official journal of the International Society for Autism Research.

[25]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[26]  Maja Pantic,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING , 2022 .

[27]  Joseph Wang,et al.  Epidermal tattoo potentiometric sodium sensors with wireless signal transduction for continuous non-invasive sweat monitoring. , 2014, Biosensors & bioelectronics.

[28]  Zhenan Bao,et al.  Biodegradable and flexible arterial-pulse sensor for the wireless monitoring of blood flow , 2019, Nature Biomedical Engineering.

[29]  Bor-Shyh Lin,et al.  Novel Wearable and Wireless Ring-Type Pulse Oximeter with Multi-Detectors , 2014, Sensors.

[30]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[31]  Kirk H. Shelley,et al.  Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[33]  Murat Akcakaya,et al.  Simple, Transparent, and Flexible Automated Quality Assessment Procedures for Ambulatory Electrodermal Activity Data , 2018, IEEE Transactions on Biomedical Engineering.

[34]  Di Wang,et al.  Reliable Breathing Tracking With Wearable Mask Device , 2020, IEEE Sensors Journal.

[35]  Nicola Cellini,et al.  Wearable Sleep Technology in Clinical and Research Settings , 2019, Medicine and science in sports and exercise.

[36]  David R Bassett,et al.  Device-based monitoring in physical activity and public health research , 2012, Physiological measurement.

[37]  Victoria Xia,et al.  Active learning for electrodermal activity classification , 2015, 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[38]  Zhenan Bao,et al.  Electronic Skin: Recent Progress and Future Prospects for Skin‐Attachable Devices for Health Monitoring, Robotics, and Prosthetics , 2019, Advanced materials.

[39]  Qiao Li,et al.  ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction , 2013, IEEE Transactions on Biomedical Engineering.

[40]  S. R. Yadhuraj,et al.  Motion Artifact Reduction in Photoplethysmographic Signals: A Review , 2013 .

[41]  Nam-Trung Nguyen,et al.  Stretchable respiration sensors: Advanced designs and multifunctional platforms for wearable physiological monitoring. , 2020, Biosensors & bioelectronics.

[42]  Cesare Furlanello,et al.  DBD-RCO: Derivative Based Detection and Reverse Combinatorial Optimization to improve heart beat detection for wearable devices , 2017, bioRxiv.

[43]  John M. Graybeal,et al.  The Effect of Motion on Pulse Oximetry and Its Clinical Significance , 2007, Anesthesia and analgesia.

[44]  Rosalind W. Picard,et al.  Empatica E3 — A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition , 2014, 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH).

[45]  Jean-Yves Fourniols,et al.  Smart wearable systems: Current status and future challenges , 2012, Artif. Intell. Medicine.

[46]  Jae‐Woong Jeong,et al.  Advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment , 2019, Advanced materials.

[47]  Joris H. Janssen,et al.  Emotional sweating across the body: Comparing 16 different skin conductance measurement locations , 2012, Physiology & Behavior.

[48]  R. van Hillegersberg,et al.  Vital Signs Monitoring with Wearable Sensors in High-risk Surgical Patients , 2019, Anesthesiology.

[49]  M. Bornstein,et al.  Physical presence of spouse enhances brain-to-brain synchrony in co-parenting couples , 2020, Scientific Reports.

[50]  M. Malik.  Heart Rate Variability , 1996, Clinical cardiology.

[51]  Akane Sano,et al.  Automatic identification of artifacts in electrodermal activity data , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[52]  Jean-Yves Reginster,et al.  The promise of wearable activity sensors to define patient recovery , 2014, Journal of Clinical Neuroscience.

[53]  Jeremiah D. Wander,et al.  A combined segmenting and non-segmenting approach to signal quality estimation for ambulatory photoplethysmography , 2014, Physiological measurement.