Multimodal Stress Classification Based on Biosignals Extracted from Smart Devices and Electromyography

Maria Justino, Phillip Probst, Daniel Zagalo, Cátia Cepeda, Hugo Gamboa

2023

Abstract

Work-Related Stress is the second most impactful occupational health problem in Europe, behind musculoskeletal diseases. When mental health is adequately handled, a worker’s well-being, performance, and productivity can be considerably improved. This paper presents machine learning models to classify mental stress experienced by office workers using physiological signals including heart rate, acquired using a smartwatch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius electromyography, using proprietary electromyography sensors. Two interactive protocols were implemented to collect data from 12 individuals. Time features were extracted from heart rate and electromyography signals, with frequency features also being extracted from the latter. Statistical and temporal features were extracted from the derived respiration signal. Different input modalities were tested for the machine learning models: one for each physiological signal and a multimodal one, combining all of them. Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor were employed for mental stress classification. Random Forest obtained the best results (67.7%) for the heart rate model whereas K-Nearest-Neighbor attained higher accuracies for the respiration (89.1%) and electromyography (95.4%) models. Both algorithms achieved 100% accuracy for the multimodal model. A possible future approach would be to validate these models in real time.

Paper Citation

in Harvard Style

Justino M., Probst P., Zagalo D., Cepeda C. and Gamboa H. (2023). Multimodal Stress Classification Based on Biosignals Extracted from Smart Devices and Electromyography. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 265-272. DOI: 10.5220/0011687200003414

in Bibtex Style

@conference{biosignals23,
author={Maria Justino and Phillip Probst and Daniel Zagalo and Cátia Cepeda and Hugo Gamboa},
title={Multimodal Stress Classification Based on Biosignals Extracted from Smart Devices and Electromyography},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={265-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011687200003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Multimodal Stress Classification Based on Biosignals Extracted from Smart Devices and Electromyography
SN - 978-989-758-631-6
AU - Justino M.
AU - Probst P.
AU - Zagalo D.
AU - Cepeda C.
AU - Gamboa H.
PY - 2023
SP - 265
EP - 272
DO - 10.5220/0011687200003414
PB - SciTePress