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<p>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.</p>

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Prediction of Self-Care Behaviors in Patients Using High-Density Surface Electromyography Signals and an Improved Whale Optimization Algorithm-Based LSTM Model

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Shuai Huang,
  • Dan Liu,
  • Youfa Fu,
  • Jiadui Chen,
  • Ling He,
  • Jing Yan,
  • Di Yang

摘要

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.