Abstract: Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments. Traditional surface electromyography (sEMG) analysis typically focuses on isolated hand postures, overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence. This study introduces a novel framework that combines high-density sEMG (HD-sEMG) signals with an improved Whale Optimization Algorithm (IWOA)-optimized Long Short-Term Memory (LSTM) network to address this limitation. The key contributions of this work include: (1) the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors, along with time and posture markers, to better reflect real-world patient interactions; (2) the development of a multi-channel feature fusion module based on Pascal’s theorem, which enables efficient signal segmentation and spatial–temporal feature extraction; and (3) the enhancement of the IWOA algorithm, which integrates optimal point set initialization, a diversity-driven pooling mechanism, and cosine-based differential evolution to optimize LSTM hyperparameters, thereby improving convergence and global search capabilities. Experimental results demonstrate superior performance, achieving 99.58% accuracy in self-care behavior recognition and 86.19% accuracy for 17 continuous gestures on the Ninapro db2 benchmark. The framework operates with low latency, meeting the real-time requirements for assistive devices. By enabling precise, context-aware recognition of daily activities, this work advances personalized rehabilitation technologies, empowering stroke patients to regain autonomy in self-care tasks. The proposed methodology offers a robust, scalable solution for clinical applications, bridging the gap between laboratory-based gesture recognition and practical, patient-centered care.

Key words: Self-care behaviors')">Self-care behaviors, High-density surface electromyography (HD-sEMG), Long Short-Term Memory (LSTM) network, Multi-channel feature fusion