Modern myoelectric prostheses remain difficult to control, particularly during rehabilitation, leading to high abandonment rates in favor of static devices. This highlights the need for advanced controllers that can automate some motions. This study presents an end-to-end framework coupling deep reinforcement learning with augmented reality (AR) for prosthetic actuation. A 14-degree-of-freedom hand was modeled in Blender and deployed in Unity. Two reinforcement learning agents were trained with distinct reward functions for a grasping task: (i) a discrete, Booleann reward with contact penalties and (ii) a continuous distance-based reward between joints and the target object. Each agent trained for 3 × 10⁷ timesteps at 50 Hz. The Booleann reward function performed poorly by entropy and convergence metrics, while the continuous reward function achieved success. The trained agent using the continuous reward was integrated into a dynamic AR scene, where a user controlled the prosthesis via a myoelectric armband while the grasping motion was actuated automatically. This framework demonstrates potential for assisting patients by automating certain movements to reduce initial control difficulty and improve rehabilitation outcomes.

As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and the emerging trends in intelligent wheelchair control. A specific focus is provided on classifying and comparing model-driven and data-driven control methodologies. In this review, findings from a range of past contributions are examined, including conventional control theories, rule-based systems, and modern data-driven approaches that include supervised, unsupervised, and reinforcement learning control algorithms. The analysis indicates that while model-driven methods offer interpretability, data-driven techniques—in particular those leveraging machine learning—provide for a superior adaptability for navigating complex and dynamic environments. We further highlight key supporting systems found in sensors, actuators, and human-machine interfaces. Additionally, the important functionalities such as autonomous navigation and obstacle avoidance methods are identified. Our findings point to some future objectives that need to be addressed. For example, energy efficiency, robustness in unpredictable settings, computational requirements, and associated demands when utilizing data-driven methods. One of the highlighted fields of study in this work is the integration of reinforcement learning and sensor fusion, which may hold some promising results for future wheelchair technologies.