Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 11; p. 5220
• Main Authors: Dahal, Kamana, Bogue-Jimenez, Brian, Doblas, Ana
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 31.05.2023; MDPI
• Subjects: Accuracy; Algorithms; Datasets; ECG; Electrocardiography; feature selection; global training; Health aspects; Heart diseases; Heart rate; Hormones; HRV features; Hypertension; Physiology; Questionnaires; Sensors; Stress; Stress (Psychology); stress detection; Wearable computers; wearable device; wearable device; individual testing; ECG; global training; machine learning; feature selection; stress detection; HRV features

Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high...