Deep parallel spatial-temporal cross-attention network for continuous joint angle prediction using sEMG signals across multiple terrains

Le résumé fourni par la source

Accurate decoding of user motion intention from surface electromyography (sEMG) signals is a critical challenge in the control of lower limb exoskeletons, particularly under non-stationary conditions and across complex terrains. Existing deep learning methods often struggle to effectively capture multi-granular signal features and decouple complex spatiotemporal dependencies, limiting their robustness. To address these issues, this paper proposes a Deep Parallel Spatial-Temporal Cross-Attention Network (PST-CAN) for continuous prediction of lower limb joint angles. First, a Multi-Scale Xception (MS-Xception) encoding module is designed to adaptively extract cross-scale spatiotemporal features from raw sEMG signals using parallel depthwise separable convolutions. Second, a Cascaded Parallel Spatiotemporal Dual Cross-Attention (PST-DCA) mechanism is introduced. By employing a parallel dual-branch structure, this mechanism explicitly decouples and models spatial muscle synergies and temporal evolution patterns, mitigating information loss associated with traditional serial fusion strategies. Experimental validation on the multi-terrain ENABL3S dataset demonstrates that PST-CAN achieves an average MAE of 2.59 \(^{\circ }\) , an RMSE of 4.19 \(^{\circ }\) , an \(R^2\) of 0.961, and a CC of 0.980 under the revised chronological training/validation/testing protocol. The proposed framework achieves the best overall performance among the evaluated baseline models and maintains robust trajectory tracking and phase alignment across complex locomotion conditions.

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