Decoding Fatigue: Analyzing Offline Handwriting with Machine Learning to Detect Perceived Exhaustion
Schoen, Dominik and Kosch, Thomas and Becker, Till and Antwi-Boasiako, Godfred and Jung, Merret and Chioca Vieira, Ana Laura and Mühlhäuser, Max and Müller, Florian
Abstract: The quality and readability of an individual’s handwriting and drawing can be influenced by various factors, including their level of physical exertion. This enables us to explore the quantification of exertion by observing an individual’s handwriting. To test this hypothesis, we collected data from 17 participants, building a database of handwriting and drawing samples and their corresponding Borg 10 exertion ratings at the time of drawing. In this paper, we investigate using machine learning techniques to estimate perceived exertion before, during, and after physical activity based on handwriting and drawings. We apply a regression model to compare different drawing tasks and demonstrate that perceived exertion can be predicted using simple line drawings. However, more complex sketches and handwriting demand further research. Our findings suggest that interactive systems could use handwriting and drawing to intervene when users experience excessive discomfort.
@inproceedings{schoenDecodingFatigueAnalyzing2024,title={Decoding {{Fatigue}}: {{Analyzing Offline Handwriting}} with {{Machine Learning}} to {{Detect Perceived Exhaustion}}},shorttitle={Decoding {{Fatigue}}},booktitle={Proceedings of the {{International Conference}} on {{Mobile}} and {{Ubiquitous Multimedia}}},author={Schoen, Dominik and Kosch, Thomas and Becker, Till and {Antwi-Boasiako}, Godfred and Jung, Merret and Chioca Vieira, Ana Laura and M{\"u}hlh{\"a}user, Max and M{\"u}ller, Florian},year={2024},month=dec,series={{{MUM}} '24},pages={487--489},publisher={Association for Computing Machinery},address={New York, NY, USA},doi={10.1145/3701571.3703393},urldate={2024-12-03},isbn={979-8-4007-1283-8},}
