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<p>This Systematic Literature Review (SLR) critically analyzes the application of reinforcement learning (RL) algorithms for controlling grasp and release movements in myoelectric hand prostheses. Following Kitchenham’s methodology and incorporating PRISMA-inspired reporting principles, 72 primary studies published between 2019 and 2025 were systematically identified, evaluated, and synthesized to examine RL control strategies, algorithmic trends, reward function design, and evaluation metrics in prosthetic control systems. The findings indicate that RL has emerged as a promising approach for adaptive prosthetic control, particularly in dynamic environments characterized by noisy electromyographic (EMG) signals, variable task conditions, and user-specific motor behavior. Algorithms such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) were frequently associated with favorable reported performance due to their balance between stability, adaptability, and learning efficiency. However, the review also reveals a critical lack of standardized reward engineering and evaluation protocols, which limits reproducibility, cross-study comparability, and clinical translation. To address this gap, this review introduces taxonomy-driven analyses of reward function strategies and evaluation metrics, highlighting the predominance of generic reward formulations, heterogeneous validation settings, and limited real-world testing with prosthetic users. Overall, this work provides a structured and critical synthesis of current trends, methodological limitations, and future research directions for developing reproducible, clinically relevant, and intelligent RL-based prosthetic control systems.</p>

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Systematic Literature Review on the Development of Reinforcement Learning Algorithms Applied to the Operation of a Myoelectric Prosthesis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月24日

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  • Mario Moreno-Pallares,
  • Lorena Isabel Barona López,
  • Ángel Leonardo Valdivieso Caraguay,
  • Marco E. Benalcázar

摘要

This Systematic Literature Review (SLR) critically analyzes the application of reinforcement learning (RL) algorithms for controlling grasp and release movements in myoelectric hand prostheses. Following Kitchenham’s methodology and incorporating PRISMA-inspired reporting principles, 72 primary studies published between 2019 and 2025 were systematically identified, evaluated, and synthesized to examine RL control strategies, algorithmic trends, reward function design, and evaluation metrics in prosthetic control systems. The findings indicate that RL has emerged as a promising approach for adaptive prosthetic control, particularly in dynamic environments characterized by noisy electromyographic (EMG) signals, variable task conditions, and user-specific motor behavior. Algorithms such as Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) were frequently associated with favorable reported performance due to their balance between stability, adaptability, and learning efficiency. However, the review also reveals a critical lack of standardized reward engineering and evaluation protocols, which limits reproducibility, cross-study comparability, and clinical translation. To address this gap, this review introduces taxonomy-driven analyses of reward function strategies and evaluation metrics, highlighting the predominance of generic reward formulations, heterogeneous validation settings, and limited real-world testing with prosthetic users. Overall, this work provides a structured and critical synthesis of current trends, methodological limitations, and future research directions for developing reproducible, clinically relevant, and intelligent RL-based prosthetic control systems.