Leiden University Scholarly Publications

Thammasan, N.; Stuldreher, I.V.; Schreuders, E.; Giletta, M.; Brouwer, A.M. (2020)

A usability study of physiological measurement in school using wearable sensors

Article / Letter to editor

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal...Show moreMeasuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.
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Wearables

EDA

Heart Rate

Peripheral Physiology

Ambulatory Monitoring

All authors

Thammasan, N.; Stuldreher, I.V.; Schreuders, E.; Giletta, M.; Brouwer, A.M.

Date

2020-09-20

Journal

Sensors

Volume

20

Issue

18

Pages

5380

DOI

doi:10.3390/s20185380

Link

https://www.mdpi.com/1424-8220/20/18/5380

Funding

Sponsorship

NWO

Grant number

400.17.602