Stress Assessment with Convolutional Neural Network Using PPG Signals

TL;DR: This paper introduces an innovative approach to recognizing stress and meditation emotions, building upon prior research in the field, and focuses on Photoplethysmogram signals, known for their sensitivity to stress-related physiological changes, and employs Continuous Wavelet Transform analysis for a comprehensive exploration of stress-related patterns.

Abstract: Stress is a state that occurs when an individual's physical and mental resources are taxed in response to demands, becoming especially evident under heavy mental exertion. Mental workload is a significant psychophysiological metric that directly influences task performance and can also lead to mental diseases such as depression. Thus, the objective evaluation of stress levels using physiological data is crucial for enhancing work productivity and assuring safety. This work employed an integrated approach utilizing electrocardiography (ECG) and photoplethysmography (PPG) signals for stress detection. The data were sourced from the publically accessible MAUS dataset and gathered from 22 healthy participants utilizing wearable sensors during N-back activities. The signals were segmented into epochs, and a total of 50 features were extracted at both temporal and spectral levels. The features were examined utilizing diverse machine learning algorithms. The models' performance is assessed using accuracy, specificity, F-score, and AUC criteria, with the Bagged Trees method achieving the greatest accuracy of 98.6%. The results indicate that employing several biosignals and sophisticated signal processing techniques provides excellent precision in stress detection. The device provides a pragmatic option for real-time monitoring of individuals' stress levels in their daily lives, thanks to its portable design.

TL;DR: This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field.

TL;DR: This work introduces WESAD, a new publicly available dataset for wearable stress and affect detection that bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement).

TL;DR: Some of the current developments and challenges of wearable PPG-based monitoring technologies are considered and some of the potential applications of this technology in clinical settings are discussed.

TL;DR: Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.