Physical, Social and Cognitive Stressor Identification using Electrocardiography-derived Features and Machine Learning from a Wearable Device

Keyphrases

• Machine Learning 100%
• Social Stressors 100%
• Wearable Devices 100%
• Feature Learning 100%
• Physical Stressors 100%
• Cognitive Stressor 100%
• Stressor Identification 100%
• College Students 66%
• Monitoring System 66%
• Multi-class Classification 66%
• Older Adults 33%
• Continuous Monitoring 33%
• Physiological Changes 33%
• Classification Accuracy 33%
• Binary Classification 33%
• Support Vector Machine 33%
• Anxiety Symptoms 33%
• Multi-class 33%
• Heart Rate Variability 33%
• Machine Learning Models 33%
• Financial Concerns 33%
• Symptom Detection 33%
• Stressful Conditions 33%
• Mental Health Conditions 33%
• Individualized Treatment 33%
• ML Classifiers 33%
• Binary Class 33%
• Relationship Concern 33%
• Hexoskin 33%
• Academic Pressure 33%

Computer Science

• Representation Learning 100%
• Wearable Device 100%
• Machine Learning 100%
• Learning System 100%
• Electrocardiography 100%
• Classification Accuracy 50%
• Multiclass Classification 50%
• Monitoring Tool 50%
• Multi Class Classification 50%
• Classification Performance 25%
• Continuous Monitoring 25%
• Support Vector Machine 25%
• Generalizability 25%
• Health Condition 25%
• Financial Concern 25%
• Binary Classification 25%

Psychology

• Stressors 100%
• College Students 40%
• Mental Health 20%
• Interpersonal Relationship 20%
• Learning Model 20%