WristSense: Sensing Hidden Wrist Strain in Routine Activities via Inertial Tokenization and LLM-Based Feedback

Wrist micro-behaviors during daily activities such as typing, handwriting, cooking, or carrying objects are valuable indicators for early detection of wrist disorders like Carpal Tunnel Syndrome and tendonitis. However, continuous personalized monitoring remains challenging without intrusive setups or hand-crafted rules. We present WristSense, a real-time, wrist-worn sensing system that introduces: (i) a magnetometer-stabilized quaternion fusion pipeline for orientation-agnostic tracking, (ii) a lightweight 1D-CNN + HMM model to distinguish functional gestures from strain-related coping behaviors, and (iii) an inertial tokenization scheme that converts events into structured prompts for a pretrained LLM. This enables zero-shot ergonomic feedback without per-user calibration. Evaluations across 12 participants show accurate posture tracking (< 10° MAE), high gesture recognition (macro F1 = 0.91), and improved usability (SUS = 85.2), with significantly higher user compliance compared to rule-based methods. WristSense demonstrates the potential of combining inertial sensing with LLMs for scalable, personalized ergonomic monitoring and early intervention.