Real-Time Continuous Calibration of an EMG-Informed Neuromusculoskeletal Model for Assistive Exoskeleton Control

Accurate real-time neuromusculoskeletal (NMS) modeling is crucial for closed-loop neurorehabilitation. This study presents a novel electromyography (EMG)-informed NMS modeling framework with continuous real-time calibration for controlling an upper limb exoskeleton. Using an autodifferentiable NMS model, we implemented a sliding window online calibration to eliminate the need for model calibration prior to use. The framework was validated during a functional reaching task performed with an ArmeoPower exoskeleton. Model accuracy and exoskeleton control performance were compared across uncalibrated, offline calibrated, and continuously calibrated models. The continuously calibrated model achieved comparable accuracy to the offline calibrated model within just 15 movement cycles (110 seconds), eliminating the need for pre-session calibration. Furthermore, the continuous calibration approach provided physiologically plausible predictions of joint moments, enabling exoskeleton control performance comparable to the offline calibrated NMS model. An offline synergy-driven modelling approach was also examined, demonstrating potential to represent unrecorded excitations and improve moment prediction accuracy. By addressing the limitations of prior methods, this framework enhances the usability of EMG-informed NMS models in realtime applications, offering a personalized and adaptive solution for neurorehabilitation, able to dynamically adapt to physiological changes such as fatigue.