To maintain overall health and well-being, it is crucial to manage mental stress. This study
focuses on developing a deep learning model for recognizing mental stress levels using the sensors of
smartwatches. Most related research with notable performance has focused on mental stress detection
using various physiological biomarkers obtained through sophisticated IoMT (Internet of Medical Things)
devices. However, the ones using only the smartwatch’s measurable physiological biomarkers, which
do not include respiration rate, have comparatively lower performance because of a limited number of
physiological biomarkers. In this paper, we introduce an improved model for mental stress detection using
boosting neural network that can be integrated into a smartwatch. The proposed model consists of two
phases. In the first phase, we introduce a boosting neural network technique that predicts the respiration rate
by 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 respiration
rate, is used for stress level classification via an artificial neural network. The necessary hyperparameter
tuning is performed to obtain the optimal values of various model parameters. The training of the model
is performed for fifteen different subjects of the publicly available multimodal WESAD (Wearable Stress
and Affect Detection) dataset using various biomarkers measured by smartwatches. The proposed model
predicts respiration rate with low error (0.035 MSE (Mean Squared Error)) and achieves high mental stress
detection accuracy of 94% using smartwatch measurable biomarkers which is a ∼2% improvement over the
current contemporary technique.