ErgoAssist: Cognition-Aware Posture Feedback in Wearable Ergonomic Systems

Existing ergonomic wearables often rely on simple posture thresholds, which frequently trigger alerts during high-focus tasks. This leads to alert fatigue, user annoyance, and eventual abandonment. The authors propose ErgoAssist, a head-worn system that integrates IMU-based posture sensing with EEG-based cognitive load estimation. By monitoring both physical strain and mental workload, the system aims to deliver interventions only when users are most receptive, such as during natural breakpoints or periods of low cognitive demand.

In a controlled lab study, ErgoAssist achieved 81% accuracy in posture classification and 90.2% accuracy in task-induced cognitive load estimation. A preliminary real-time deployment demonstrated that cognition-aware alerting is significantly more effective than posture-only feedback. Specifically, the system reduced alert frequency by 81%, improved perceived usability by 43%, and increased the posture correction rate by 38%. Furthermore, participants showed a 25% improvement in task performance, suggesting that better-timed interventions minimize the disruption caused by traditional ergonomic alerts.

This research shifts the paradigm of ergonomic assistance from purely physical monitoring to context-aware interaction. By recognizing that posture and cognition are tightly coupled, ErgoAssist demonstrates that ergonomic systems can be both more effective and less intrusive. This approach addresses the fundamental challenge of user compliance in wearable technology, offering a blueprint for designing assistants that respect user focus while promoting long-term musculoskeletal health.