Comparison of wearable and clinical devices for experiments with multivariate physiological signals

Abstract

The development of wearable technologies enables the acquisition and quantification of physiological signals in a wide range of contexts, from personal uses to clinical and scientific research. Wearable Devices (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 methods and scientific software for the assessment of WDs for multivariate physiological signals. The quality of cardiac and Electrodermal Activity signals is technically validated with a standard set of Signal Quality Indicators, available as open source in the PyPhisio Python library. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device (Flex Comp, Thought Technology) and two WDs (E4, Empatica and HeartBand, ComfTech). 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. Software and data and software are made available.