A Preliminary Experimental Outline to Train Machine Learning Models for the Unobtrusive, Real-Time Detection of Acute Physiological Stress Levels during Training Exercises André Jeworutzki Jan Schwarzer Kai von Luck University of the West of Scotland Hamburg University of Applied Hamburg University of Applied Hamburg University of Applied Sciences Sciences Sciences Hamburg, Germany Hamburg, Germany Hamburg, Germany
[email protected] [email protected] [email protected] Susanne Draheim Qi Wang Hamburg University of Applied University of the West of Scotland Sciences Paisley, Scotland Hamburg, Germany
[email protected] [email protected] ABSTRACT KEYWORDS The automatic recognition of activities can assist people in keeping activity recognition, stress determination, physiological sensors, track of their health and in avoiding injuries. Nowadays, inertial inertial measurement units, supervised machine measurement units have gained notable interest for such tasks due to being low-cost, small-sized, and easy-of-use. Inertial sensor ACM Reference Format: André Jeworutzki, Jan Schwarzer, Kai von Luck, Susanne Draheim, and Qi technology in combination with physiological data allows to state Wang. 2021. A Preliminary Experimental Outline to Train Machine Learning holistic conclusions regarding, for example, an activity’s quality. Models for the Unobtrusive, Real-Time Detection of Acute Physiological This research draws attention to the case of stress levels in sports, Stress Levels during Training Exercises. In The 14th PErvasive Technologies where researchers typically rely on obtrusive stress markers an- Related to Assistive Environments Conference (PETRA 2021), June 29-July 2, alyzed in laboratories (e.g., lactate and cortisol). While there are 2021, Corfu, Greece.