StitchSense: Smart-Garment Sensing as a Source Domain for Watch-Based Activity Recognition

Wearable computing was originally imagined as computation woven into what people already wear, yet HAR has largely advanced through rigid or semi-rigid devices such as smartphones, smartwatches, and strapped IMUs, where sensor motion closely tracks body motion. Smart garments revisit this clothing-integrated vision while challenging this assumption, since fabric-mounted sensors also capture garment motion, fit variation, and sensor displacement. We present StitchSense, a multi-IMU smart vest for studying garment-mounted activity recognition and transfer to wrist-worn HAR. StitchSense places five IMUs at the shoulders, chest, and hips to capture upper-body, trunk, and locomotion-related movement. We evaluate it both as a direct garment-based HAR platform and as a source domain for watch-based recognition under limited target calibration. Results show that StitchSense captures coarse functional movement structure, but standalone fine-grained recognition remains challenging due to garment motion, fit variation, sensor instability, and participant variability. In transfer experiments, combining StitchSense source data with limited target-watch calibration improves watch-based recognition, suggesting that smart garments can complement rather than replace wrist-worn devices.

Verbatim fromSemantic Scholar· Abstract via Semantic Scholar; © the authors/publisher