Toward soft real-time stress detection using wrist-worn devices for human workspaces

Continuous exposure to stress leads to many health problems and substantial economic loss in companies. A lot of attention has been given to the development of wearable systems for stress monitoring to tackle its long-term effects such as confusion, high blood pressure, insomnia, depression, headache and inability to take decisions. Accurate detection of stress from physiological measurements embedded in wearable devices has been the primary goal in the healthcare industry. Advanced sensor devices with a high sampling rate have been proven to achieve high accuracy in many earlier works. However, there has been a little attempt to employ consumer-based devices with a low sampling rate, which potentially degrades the performance of detection systems. In this paper, we propose a set of new features, local maxima and minima (LMM), from heart rate variability and galvanic skin response sensors along with the voting and similarity-based fusion (VSBF) method, to improve the detection performance. The proposed feature set and fusion method are first tested on the acquired dataset which is collected using the wrist-worn devices with a low sampling rate in workplace environments and validated on a publicly available dataset, driveDB from PhysioNet. The experimental results from both datasets prove that the LMM features can improve the detection accuracy for different classifiers in general. The proposed VSBF method further boosts the recognition accuracy by 5.69% and 2.90% in comparison with the AdaBoost, which achieves the highest accuracy as a single classifier on the acquired, and the DriveDB dataset, respectively. Our analyses show that the stress detection system using the acquired dataset yields an accuracy of 92.05% and an F 1 score of 0.9041. Based on the analyses, a soft real-time system is implemented and validated to prove the applicability of the proposed work for stress detection in a real environment.