Smartwatch-Based Sitting Time Estimation in Real-World Office Settings
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913020912861184 |
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| author | Zhang, Olivia Zhang, Zhilin |
| author_facet | Zhang, Olivia Zhang, Zhilin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08808 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Zhang, Olivia Zhang, Zhilin Machine Learning Human-Computer Interaction 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. |
| title | Smartwatch-Based Sitting Time Estimation in Real-World Office Settings |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.08808 |