A Novel Framework of Hierarchical EMG-FMG Fusion to Enhance Long-term and Multi-position Robustness for Real-Time Motion Intent Recognition.

Abstract

Prosthetics, exoskeletons, and rehabilitation devices that seamlessly respond to the user's motion intent are essential for improving the quality of life for individuals with physical disabilities. However, existing motion intent recognition systems based on Electromyography (EMG) often experience significant performance degradation over time and limb positions. To address this, this paper proposed a Hierarchical EMG-FMG Fusion (HEFF) framework that incorporates Force Myography (FMG) as a complementary modality to enhance robustness in long-term and multi-position motion intent recognition. The HEFF framework introduced three new strategies: (1) Complementary Pyramid Fusion, a neural network architecture that effectively integrates the complementary characteristics of EMG and FMG; (2) Position Compensation and Pairing, to increase the generalizability of the model by producing augmented training data mimicking the temporal and positional variability; and (3) Feedback-based Baseline Correction, to dynamically counteract FMG baseline drift. The robustness of the system was evaluated through 10 distinct motions performed in 3 different limb positions, retested after an interval of approximately 7 days. Notably, the training data were only collected in a single limb position on the first day. The real-time experiments with 15 subjects showed that the HEFF framework maintained 91.89% classification accuracy in the primary limb position and 85.39% across multiple unseen positions. According to offline analysis, HEFF improved classification accuracy by up to 34.99% across various limb positions compared to the conventional fusion method. These results highlight HEFF's robustness across various conditions, being well-suited for assistive technology applications.