International Journal of Scientific Research and Engineering Development

Mobility impairment resulting from neurological disorders, musculoskeletal injuries, and age-related decline remains a critical challenge affecting millions worldwide. Wearable robotic exoskeletons have emerged as an essential rehabilitation tool, offering repetitive and controlled movement patterns that promote strength recovery, gait correction, and improved independence. Building on this potential, this study introduces an IoT-enabled exoskeleton architecture derived from the methodological principles of the ExoLimb framework. The proposed system emphasizes lightweight mechanical design, sensor-driven control, environmental adaptability, and continuous data monitoring to support both clinical and homebased rehabilitation. The architecture incorporates actuated lower-limb joints, multi-sensor fusion for gait phase detection, and physiological data collection through integrated wearable sensors. An IoT communication layer using secure protocols enables real-time data transmission to a cloud-based platform, where therapists can remotely analyze mobility performance, track rehabilitation progress, and adjust treatment intensity as needed. By extending the ExoLimb methodology, this research enhances motion accuracy, increases responsiveness to user intent, and strengthens the scalability of rehabilitation support systems. Simulation-based evaluations demonstrate reduced joint-angle error, improved gait classification accuracy, and reliable low-latency communication suitable for dynamic rehabilitation environments. These results validate that an IoT-integrated exoskeleton can offer an affordable, adaptable, and data-driven solution for individuals requiring long-term mobility assistance. The proposed architecture lays the foundation for future prototype development, machine learning–based gait prediction, and clinical testing in diverse patient populations.