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.2023 Jun 23;23(13):5847.
doi: 10.3390/s23135847.

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3

Affiliations

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3

Vincenzo Ronca et al. Sensors (Basel)..

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.

Keywords: consumer wearables; electrodermal activity; photoplethysmography.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the three different devices type investigated in the present work.
Figure 2
Figure 2
Overview of the experimental settings.
Figure 3
Figure 3
Person’s correlations between the Empatica E4 and Shimmer (left bar) and the Fitbit Sense and the Shimmer (right bar) in terms of Heart Rate estimation.
Figure 4
Figure 4
Person’s correlations between the Empatica E4 and Shimmer GSR3+ (left bar) and the Fitbit Sense and the Shimmer GSR3+ (right bar) in terms of Skin Conductance Level estimation.
Figure 5
Figure 5
Repeated measures correlation between the Skin Conductance Level estimated through the Empatica E4 and the Shimmer GSR3+. Lines and dots characterized by the same colour represent the data distribution per each participant.
Figure 6
Figure 6
Repeated measures correlation between the Heart Rate estimated through the Empatica E4 and the Shimmer GSR3+. Lines and dots characterized by the same colour represent the data distribution per each participant.
Figure 7
Figure 7
Repeated measures correlation between the Skin Conductance Level estimated through the Fitbit Sense and the Shimmer GSR3+. Lines and dots characterized by the same colour represent the data distribution per each participant.
Figure 8
Figure 8
Repeated measures correlation between the Heart Rate estimated through the Fitbit Sense and the Shimmer GSR3+. Lines and dots characterized by the same colour represent the data distribution per each participant.
See this image and copyright information in PMC

References

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    1. Ronca V., Giorgi A., Rossi D., Di Florio A., Di Flumeri G., Aricò P., Sciaraffa N., Vozzi A., Tamborra L., Simonetti I., et al. A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare. Sensors. 2021;21:1607. doi: 10.3390/s21051607. - DOI - PMC - PubMed
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    1. Zhu L., Spachos P., Gregori S. 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. 22–24 June 2022; - DOI

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