Authors:
Shiyao Zhang
1
;
Sergei Kolensnikov
2
;
Till Rennspieß
2
;
Robert Porzel
2
;
Tanja Schultz
1
and
Hui Liu
1
Affiliations:
1
Cognitive System Lab, University of Bremen, Bibliothekstraße 1, Bremen, Germany
;
2
Digital Media Lab, University of Bremen, Bibliothekstraße 1, Bremen, Germany
Keyword(s):
Motivation, Self-Determination, SDT, Biosignals, ECG, sEMG, Respiration Rate, LSTM, HRV Analysis, Causal Relationship.
Abstract:
Motivational dynamics in jogging constitute a pivotal factor influencing a runner’s performance, persistence, and overall engagement in the running activity. The manifestation of diminished motivation is concomitant with a cascade of physiological responses, capable of being represented through biological signals, for which biosignal monitoring, a common practice in evaluating athletic performance, emerges as a valuable tool. Should biosignals, as dynamic indicators during exercise, exhibit discernible shifts correlating with changes in motivation, the prospect of actively modulating motivation levels and intervening in athletes’ performance during exercise becomes feasible. This study consists of collecting comprehensive biological data, including electrocardiogram (ECG), surface electromyogram (sEMG), and respiration signals (RSP), from runners who participated in a 20-minute running session. Participants were asked to self-report a decrease in motivation during jogging. Using hear
t rate variability analysis, self-similarity matrix and deep learning methodologies, this work seeks to explore whether the discomforts reported and triggered by decreased motivation had discernible effects on monitored physiological signals, thus advancing our understanding of the nuanced relationship between physiological responses and motivational states in running.
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