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@article{Kumar:2024:10.1109/ACCESS.2024.3461588,
author = {Kumar, S and Chauhan, AR and Akhil and Kumar, A and Yang, G and Yang, G},
doi = {10.1109/ACCESS.2024.3461588},
journal = {IEEE Access},
pages = {149861--149874},
title = {Resp-BoostNet: mental stress detection from biomarkers measurable by smartwatches using boosting neural network technique},
url = {http://dx.doi.org/10.1109/ACCESS.2024.3461588},
volume = {12},
year = {2024}
}

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TY  - JOUR
AB  - To maintain overall health and well-being, it is crucial to manage mental stress. This studyfocuses on developing a deep learning model for recognizing mental stress levels using the sensors ofsmartwatches. Most related research with notable performance has focused on mental stress detectionusing various physiological biomarkers obtained through sophisticated IoMT (Internet of Medical Things)devices. However, the ones using only the smartwatch’s measurable physiological biomarkers, whichdo not include respiration rate, have comparatively lower performance because of a limited number ofphysiological biomarkers. In this paper, we introduce an improved model for mental stress detection usingboosting neural network that can be integrated into a smartwatch. The proposed model consists of twophases. In the first phase, we introduce a boosting neural network technique that predicts the respiration rateby utilizing the biomarkers measurable by a smartwatch. The second phase uses the In the second phase,the modified set of biomarkers, which includes both the original biomarkers and the predicted respirationrate, is used for stress level classification via an artificial neural network. The necessary hyperparametertuning is performed to obtain the optimal values of various model parameters. The training of the modelis performed for fifteen different subjects of the publicly available multimodal WESAD (Wearable Stressand Affect Detection) dataset using various biomarkers measured by smartwatches. The proposed modelpredicts respiration rate with low error (0.035 MSE (Mean Squared Error)) and achieves high mental stressdetection accuracy of 94% using smartwatch measurable biomarkers which is a ∼2% improvement over thecurrent contemporary technique.
AU  - Kumar,S
AU  - Chauhan,AR
AU  - Akhil
AU  - Kumar,A
AU  - Yang,G
AU  - Yang,G
DO  - 10.1109/ACCESS.2024.3461588
EP  - 149874
PY  - 2024///
SN  - 2169-3536
SP  - 149861
TI  - Resp-BoostNet: mental stress detection from biomarkers measurable by smartwatches using boosting neural network technique
T2  - IEEE Access
UR  - http://dx.doi.org/10.1109/ACCESS.2024.3461588
UR  - https://ieeexplore.ieee.org/document/10680515
VL  - 12
ER  -

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