Wearable Sensors for Mental Health Applications: Real-Time Stress Detection via SVM-Driven Classification by Measuring Heart Rate Variability (HRV)
DOI:
https://doi.org/10.54228/mjaret0624016Keywords:
Wearable Sensors; Heart Rate Variability (HRV); Stress Detection; Support Vector Machine (SVM); Mental Health ApplicationsAbstract
In this study we explore the use of wearable sensors to detect stress in real-time using algorithms to analyse Heart Rate Variability (HRV). The aim is to improve the monitoring of mental health and the interventions made to support people. The health data set on which this research is built is comprised of recordings of HRV of 100 participants during different tasks that triggered stress. The research finds that Support Vector Machine (SVM) classification algorithm, compared with the baseline values, provides a superior classification accuracy of 92% for HRV analysis in real-time. This, in turn, could support timely, effective interventions as stress is detected in real-time.The research, which combines wearable technology and machine learning, has implications for mental health applications.
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