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Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models.
Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020
Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.
Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being
Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits.
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
There are different methods and algorithms for stress detection using different physiological signals.
