# Smartwatch-Based Sitting Time Estimation in Real-World Office Settings

arXiv: https://arxiv.org/abs/2604.08808 · HTML: https://mlsanity.com/paper/2604.08808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches](https://mlsanity.com/paper/2509.04736/) (2025)

## Abstract (arXiv)

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.