Authors:
Maria Justino
;
Phillip Probst
;
Daniel Zagalo
;
Cátia Cepeda
and
Hugo Gamboa
Affiliation:
LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
Keyword(s):
Stress Detection, Biosignals, Occupational Health, Machine Learning, Multimodal Input.
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.
(More)