IoT-Enhanced Deep Learning Model for Elderly Motor Function Assessment in Rehabilitation Devices

This research presents a novel method for evaluating the motor function of older people in rehabilitation equipment, which makes use of the Internet of Things (IoT) and deep learning models, namely Recurrent Neural Networks (RNNs). As patients undergo rehabilitation, the IoT sensors in their equipment record vital parameters like mobility, balance, and more in real time. RNNs are trained on this processed data to identify patterns suggestive of motor activity by analyzing sequential data. During rehabilitation sessions, RNNs can evaluate the motor function of older people in real time using a dataset that includes labeled sensor data and associated evaluations. The patient and their doctor may benefit from the system's personalized recommendations, which can help with specific actions to improve motor function and rehabilitation outcomes for the elderly. The potential for this method to improve rehabilitation programs and the results achieved by older patients is substantial.