Combining Wearable Sensors and SVM to Identify Stress in Personalized Mental Health Treatment
DOI:
https://doi.org/10.54228/mjaret0624017Keywords:
Wearable sensors; Support Vector Machine (SVM); Mental health; Stress detection; Heart Rate Variability (HRV); Real-time monitoring; Personalized treatment; Machine learning; Physiological signalsAbstract
This proposed new method addresses the problem by wearable sensors with SVM classification to predict stress for personalized mental health treatment. We propose a new method that uses a wearable device for collecting real-time Heart Rate Variability (HRV) and applies the SVM algorithms of preprocessing to classify stress in real time and provide personalized interventions. The proposed method was proved to have a good effect in detecting stress in real-time. The experiments were done on a dataset collected from wearable devices. When compared with other traditional methods, the SVM model achieved an accuracy of 88%. The result indicates that the wearable sensors with the SVM method can provide personalized mental health treatment. Real-time stress detection and intervention could help individuals to live a better life.
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