A preliminary experimental outline to train machine learning models for the unobtrusive, real-time detection of acute physiological stress levels during training exercises. In PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference (pp. 575-584).

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Stress is considered to have a bidirectional relationship with exercise injuries. This PhD project addresses the lack of research on methodological and measurement standards for determining acute physiological stress in the challenging context of repetitive training exercises such as squats, push-ups, and crunches. It explores how stress levels can be determined in real-time with both inertial and heart rate sensors, and how stress levels correlate with movement quality and exhaustion. To this end, a systematic method is elaborated to build a model capable of classifying stress levels with only wearable sensors in real-time. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Applied computing → Health informatics.

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