Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data

Stress is one of the factors considerably contributing to older adult's decreasing overall health. Detecting stress in real-time could aid family members to intervene more timely and keep older adults healthier. However, many stress detection systems are not detecting in real-time, depend on multiple devices, capture a plethora of inconveniently sampled data, or use data from younger adults. In this paper, we built a real-time stress detection system for older adults using only heart beats per minute (BPM), which can be easily obtained with most single, comfortable devices. We collected data from people over 60 (N=15), evaluating the Mannheim Multicomponent Stress Test (MMST) for older adults, then built a machine learning model with a classification performance of 76% (AUC) on BPM alone and tested it in real-time in another experiment, comparing the model's effectiveness with four different heart rate devices. Detection performance decreased considerably (51%) when using the model in another experiment and could not be used successfully with other devices, while a reduced MMST induced stress comparable to the full test suite.