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

2026-04-09

AI Summary

This study addresses the critical need for accurate estimation of individual sedentary time in real-world office environments, given the well-established associations between prolonged sedentary behavior and health risks such as obesity and cardiovascular disease. The work proposes a novel approach leveraging inertial measurement unit (IMU) data from smartwatches, introducing—for the first time—a sequence of rotation vectors derived from Euler angles to characterize movement dynamics. This representation is integrated with machine learning models to enable robust sedentary behavior recognition. Evaluated on a 34-hour dataset collected in authentic office settings, the proposed method demonstrates significantly improved accuracy and environmental robustness in estimating sedentary duration compared to existing approaches.

Technology Category

Intelligent Robots: State EstimationPlanning, Routing, and Scheduling: Activity and Plan RecognitionComputer Vision: Motion & Tracking

Application Category

User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationSecurity and Privacy: Large-scale security measurementsSystems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• sitting time estimation
• sedentary behavior
• smartwatch
• inertial measurement unit
• real-world office settings

Innovation

• rotation vector sequences
• sitting time estimation
• inertial measurement unit (IMU)
• smartwatch-based sensing
• sedentary behavior monitoring