{\mu}Touch: Enabling Accurate, Lightweight Self-Touch Sensing with Passive Magnets

TL;DR

{}Touch is a lightweight magnetic sensing system that accurately recognizes micro self-touch gestures with minimal user data, suitable for health and hygiene monitoring.

Contribution

The paper introduces {}Touch, a novel magnetic sensing platform with a semi-supervised framework and ambient interference mitigation, enabling accurate micro gesture recognition.

Findings

Achieves over 93% accuracy in face-touch detection

Detects body-scratch behaviors with over 94% accuracy

Maintains performance after one month of use

Abstract

Self-touch gestures (e.g., nuanced facial touches and subtle finger scratches) provide rich insights into human behaviors, from hygiene practices to health monitoring. However, existing approaches fall short in detecting such micro gestures due to their diverse movement patterns. This paper presents {\mu}Touch, a novel magnetic sensing platform for self-touch gesture recognition. {\mu}Touch features (1) a compact hardware design with low-power magnetometers and magnetic silicon, (2) a lightweight semi-supervised framework requiring minimal user data, and (3) an ambient field detection module to mitigate environmental interference. We evaluated {\mu}Touch in two representative applications in user studies with 11 and 12 participants. {\mu}Touch only requires three-second fine-tuning data for each gesture, and new users need less than one minute before starting to use the system. {\mu}Touch can distinguish eight different face-touching behaviors with an average accuracy of 93.41%, and reliably detect body-scratch behaviors with an average accuracy of 94.63%. {\mu}Touch demonstrates accurate and robust sensing performance even after a month, showcasing its potential as a practical tool for hygiene monitoring and dermatological health applications. Code is available at https://wangmerlyn.github.io/muTouch/.