Mental stress classification during a motor task in older adults using an artificial neural network

Abstract

All people cope with mental stress from time to time. Stress can affect our emotional and physical health, which can lead to physical and/or mental health issues. Our experiment aimed to derive the stress levels of 57 older adults from the electrocardiogram (ECG) signal during a lab study that involved a hang-grip strength task. This experiment bridges the gap between previous studies by classifying the mental stress state of older adults while performing a motor task before and after the stressor was induced. In this study heart rate and heart rate variability multi-dimensional features in the time-, and frequency-domain are extracted and an optimized Artificial Neural Network (ANN) created to identify two states - - stress, or no-stress. We achieved accuracy of 90.83%.