Smartwatch-Based Sitting Time Estimation in Real-World Office Settings
For researchers and practitioners in health monitoring, this provides an incremental improvement in sitting time estimation using smartwatch data.
This work tackles the problem of estimating sitting time in real-world office settings using smartwatch IMU signals. By introducing rotation vector sequences from Euler angles, the method improves sitting time estimation, achieving better performance on a 34-hour dataset.
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.