Abstract
Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.
Highlights
The wearable technology market has mushroomed over the last decade, along with the development of the Internet of Things (IoT), and this trend shows no sign of abating
The sections detail the results obtained regarding battery life, data quality, stress indicators and physical activity from the signals captured by our proposal
We have presented a completed body area network (BAN) with low-cost wearables that supposes an excellent framework for capturing physiological signals and detecting human movements simultaneously
Summary
The wearable technology market has mushroomed over the last decade, along with the development of the Internet of Things (IoT), and this trend shows no sign of abating (https: //www.grandviewresearch.com/industry-analysis/global-wearable-sensor-market, accessed on 24 November 2021). Several studies have demonstrated its efficacy in controlling people’s weight [2], creating adherence to physical activity (PA) [3,4,5], regulating the intensity of PA; especially for those who have suffered from heart failure [6], assessing rehabilitation exercises [7,8], reducing sedentary behavior (SB) [9] for the elderly [10,11], etc. The resulting information could be used to adapt the level or intensity of the activity being performed, or change the ambient conditions where possible. Applied to PA, for example, the goal would be to create adherence to certain programs, increasing the time working out, and reducing the SB by dynamically adapting the intensity of the exercise and/or adding motivating elements to the environment
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