Intelligent Stress Assessment for e-Coaching
This addresses stress detection for individuals in emergency situations, but appears incremental as it applies existing ML techniques to a specific domain.
The paper tackled stress monitoring in e-coaching during emergencies by developing a machine learning-based assistant tool, with results from an experimental study provided.
This paper considers the adaptation of the e-coaching concept at times of emergencies and disasters, through aiding the e-coaching with intelligent tools for monitoring humans' affective state. The states such as anxiety, panic, avoidance, and stress, if properly detected, can be mitigated using the e-coaching tactic and strategy. In this work, we focus on a stress monitoring assistant tool developed on machine learning techniques. We provide the results of an experimental study using the proposed method.