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

TL;DR

This study presents a novel method using smartwatch IMU signals and rotation vector sequences to accurately estimate sitting time in real-world office environments, aiding health monitoring.

Contribution

Introduces a new approach leveraging rotation vector sequences from IMU data to improve sitting time estimation accuracy in natural settings.

Findings

Rotation vector sequences enhance estimation performance.

Method achieves better accuracy on a 34-hour dataset.

Demonstrates robustness in real-world office scenarios.

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.