Stress Detection With Single PPG Sensor by Orchestrating Multiple Denoising and Peak-Detecting Methods

Stress is one of the major causes of diseases in modern society. Therefore, measuring and managing the degree of stress is crucial to maintain a healthy life. The goal of this paper is to improve stress-detection performance using precise signal processing based on photoplethysmogram (PPG) data. PPG signals can be collected through wearable devices, but are affected by many internal and external noises. To solve this problem, we propose a two-step denoising method, to filter the noise in terms of frequency and remove the remaining noise in terms of time. We also propose an ensemble-based multiple peak-detecting method to extract accurate features through refined signals. We used a typical public dataset, namely, wearable stress and affect detection dataset (WESAD) and measured the performance of the proposed PPG denoising and peak-detecting methods by lightweight multiple classifiers. By measuring the stress-detection performance using the proposed method, we demonstrate an improved result compared with the existing methods: accuracy is 96.50 and the F1 score is 93.36%. Our code is available at https://github.com/seongsilheo/stress_classification_with_PPG.

Automated summary

This paper aims to improve stress-detection performance through precise signal processing based on PPG data, proposing a two-step denoising method and ensemble-based multiple peak-detecting method, achieving an accuracy of 96.50% and an F1 score of 93.36% on the WESAD dataset.