Abstract:Sedentary behavior poses a major public health risk, being strongly linked to obesity, cardiovascular disease, and other chronic conditions. Accurately estimating sitting time is therefore critical for monitoring and improving individual health. This work addresses the problem in real-world office settings, where signals from the inertial measurement units (IMU) on a smartwatch were collected from office workers during their daily routines. We propose a method that estimates sitting time from the IMU signals by introducing the use of rotation vector sequences, derived from Euler angles, as a novel representation of movement dynamics. Experiments on a 34-hour dataset demonstrate that exploiting rotation vector sequences improves algorithm performance, highlighting their potential for robust sitting time estimation in natural environments.
| Comments: | Accepted at the 18th International Conference on Machine Learning and Computing (ICMLC 2026), February 6-9, 2026 |
| Subjects: | Machine Learning (cs.LG); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2604.08808 [cs.LG] |
| (or arXiv:2604.08808v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08808 arXiv-issued DOI via DataCite |
From: Zhilin Zhang [view email]
[v1]
Thu, 9 Apr 2026 22:48:11 UTC (67 KB)
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