This systematic review explores recent advancements in control methods for rehabilitation exoskeleton robots, which assist individuals with motor impairments through guided movement. As robotics technology progresses, precise, adaptable, and safe control techniques have become accessible for effective human–robot interaction in rehabilitation settings. Key control methods, including computed torque and adaptive control, excel in managing complex movements and adapting to diverse patient needs. Robust and sliding mode controls address stability under unpredictable conditions. Traditional approaches, like PD and PID control schemes, maintain stability, performance, and simplicity. In contrast, admittance control enhances user–robot interaction by balancing force and motion. Advanced methods, such as model predictive control (MPC) and Linear Quadratic Regulator (LQR), provide optimization-based solutions. Intelligent controls using neural networks, Deep Learning, and reinforcement learning offer adaptive, patient-specific solutions by learning over time. This review provides an in-depth analysis of these control strategies by examining advancements in recent scientific literature, highlighting their potential to improve rehabilitation exoskeletons, and offering future recommendations for greater efficiency, responsiveness, and patient-centered functionality.

The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications.