Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+
Author:
Ronca Vincenzo12ORCID, Martinez-Levy Ana C.23, Vozzi Alessia24ORCID, Giorgi Andrea24ORCID, Aricò Pietro12ORCID, Capotorto Rossella13, Borghini Gianluca23ORCID, Babiloni Fabio235ORCID, Di Flumeri Gianluca23ORCID
Affiliation:
1. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy 2. BrainSigns Srl, 00198 Rome, Italy 3. Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy 4. Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy 5. College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310005, China
Abstract
The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human’s mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user’s activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.
Funder
European Commission Sapienza University of Rome
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference44 articles.
1. Ronca, V., Rossi, D., Di Florio, A., Di Flumeri, G., Aricò, P., Sciaraffa, N., Vozzi, A., Babiloni, F., and Borghini, G. (2020). Communications in Computer and Information Science, Springer Science and Business Media Deutschland GmbH. 2. Ronca, V., Giorgi, A., Rossi, D., Di Florio, A., Di Flumeri, G., Aricò, P., Sciaraffa, N., Vozzi, A., Tamborra, L., and Simonetti, I. (2021). A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare. Sensors, 21. 3. Signal quality and patient experience with wearable devices for epilepsy management;Nasseri;Epilepsia,2020 4. Feasibility Study of Stress Detection with Machine Learning through EDA from Wearable Devices;Zhu;IEEE Int. Conf. Commun.,2022 5. Zhu, L., Spachos, P., and Gregori, S. (2022, January 22–24). Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices. Proceedings of the 2022 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2022—Conference Proceedings, Messina, Italy.
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|