Self-Aware Fusion IMU-EMG Attention Dependence for Knee Adduction Moment Estimation During Walking

Knee osteoarthritis (KOA) as a prevalent chronic disease, detrimentally impacts the quality of life among affected individuals. The knee adduction moment (KAM) during the stance phase has been identified as a potential biomechanical measure for assessing the severity of KOA. Traditional KAM assessment relies on expensive equipment, which limits its popularization. In contrast, current KAM estimation methods based on wearables and deep-learning technology offer the advantage of lower costs. However, it still suffers challenges in achieving accurate estimation. To address this challenge, a novel deep-learning framework is proposed in this work, which estimates the KAM from Inertial Measurement Units (IMU) and Electromyography (EMG) data by a well-designed self-aware fusion model. Walking data from 18 effective subjects were recorded with 4 IMUs and 6 EMGs. Results show that the model significantly improves KAM estimation accuracy. The relative root-mean-square error of the proposed model is 9.15% BW $ \cdot $ BH lower than counterpart estimation methods.