Development of an Elderly Care Rehabilitation System Utilizing Machine Learning for Energy Monitoring and Transmission using Wireless Sensor Networks

Abstract

The combination of robotic exoskeleton systems and machine learning (ML) techniques present great potential for significantly improving rehabilitation outcomes in patients with mobility impairments. This particular study examines whether ML algorithms can predict torque and angular inputs required for joint rotation using electromyography (EMG) data obtained during patient lower-limb rehabilitation. A new exoskeleton was designed for personalized and adaptable assistance in rehabilitation of walking. The predictive power of four different ML models, Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM), is evaluated. Of these, the ANN model is the most effective with an accuracy rate of 98.87%, followed by KNN (94.55%), DT (91.2%) and SVM (89.45%). Subsequent patient tests confirm significant improvements in flexion and extension angles of the knee, suggesting improved mobility and restored natural gait mechanics. These results stress the potential uses of ML-enhanced exoskeleton systems in personalised rehabilitation therapy. Proposals for further work include improving model performance, tackling challenges for real-time processing, and evaluating ML performance over the long term as it affects the quality of life in individuals with mobility impairments or changes in their condition over time. This investigation into rehabilitation aims at applying technology-driven solutions to help people regain their freedom and ability to move in a way that pleases them.