Cross-Activity sEMG-Driven Joint Angle Estimation via Hybrid Attention Fusion: Bridging Traditional Features and Deep Spatial Representations

Abstract

The growing prevalence of stroke necessitates advanced lower-limb exoskeleton control. This paper proposes HybridFusionAtt, a novel model for continuous joint angle estimation using surface electromyography (sEMG). Unlike conventional approaches, our framework uniquely integrates traditional time-domain features with CNN-extracted high-dimensional spatial features through an attention mechanism, where traditional features dynamically guide feature fusion as attention queries. The model was validated using data collected from eight participants performing four activities of daily living (walking, stair climbing, stair descending, and obstacle crossing). The proposed model achieves average R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values for knee and hip joint angle prediction of 0.8682 (walking), 0.8482 (obstacle crossing), 0.9294 (stair climbing), and 0.8676 (stair descending). Experimental results show that the proposed model significantly outperforms traditional LSTM and CNN-LSTM models in terms of accuracy and robustness, particularly in handling non-periodic actions such as obstacle crossing. The model achieves high performance by effectively fusing features and adaptively focusing on key features, enabling it to maintain robustness even under noisy conditions and significant individual differences. This demonstrates the model’s broad application potential, especially in rehabilitation and prosthetic control systems.