Evaluating Mental Stress Among College Students Using Heart Rate and Hand Acceleration Data Collected from Wearable Sensors

TL;DR

This study demonstrates that machine learning models, especially XGBoost, can effectively identify stress episodes in college students using wearable sensor data, with potential for real-time stress monitoring.

Contribution

The paper introduces a machine learning approach utilizing heart rate and hand acceleration data from wearables for stress detection in college students, validated with real-world data.

Findings

XGBoost achieved an AUC of 0.64 and 84.5% accuracy.

Key features include hand acceleration standard deviation and heart rate metrics.

Physiological patterns can be used for real-time stress detection.

Abstract

Stress is various mental health disorders including depression and anxiety among college students. Early stress diagnosis and intervention may lower the risk of developing mental illnesses. We examined a machine learning-based method for identification of stress using data collected in a naturalistic study utilizing self-reported stress as ground truth as well as physiological data such as heart rate and hand acceleration. The study involved 54 college students from a large campus who used wearable wrist-worn sensors and a mobile health (mHealth) application continuously for 40 days. The app gathered physiological data including heart rate and hand acceleration at one hertz frequency. The application also enabled users to self-report stress by tapping on the watch face, resulting in a time-stamped record of the self-reported stress. We created, evaluated, and analyzed machine learning algorithms for identifying stress episodes among college students using heart rate and accelerometer data. The XGBoost method was the most reliable model with an AUC of 0.64 and an accuracy of 84.5%. The standard deviation of hand acceleration, standard deviation of heart rate, and the minimum heart rate were the most important features for stress detection. This evidence may support the efficacy of identifying patterns in physiological reaction to stress using smartwatch sensors and may inform the design of future tools for real-time detection of stress.