Intelligent Adaptive Control Strategies for Personalized Therapy and Improved Outcomes with Enhanced Mechatronic Rehabilitation

Abstract

This paper introduces a mechatronic rehabilitation approach focused on enhancing the efficacy and personalization of therapy sessions for individuals with mobility disabilities. The primary emphasis lies in the development of adaptive control strategies for the rehabilitation robot, aiming to deliver a personalized and responsive therapeutic experience. The proposed system facilitates active learning and adjustment of therapeutic sessions. The system is expected to simultaneously optimize therapy sessions continually customizing exercises to realtime performance data, resulting in a more personalized and successful rehabilitation experience. The proposed system describes how artificial intelligence, machine learning, and virtual sensors are integrated. The use of machine learning algorithms promises improved performance on specific tasks over time as it is exposed to more data, whereas artificial intelligence is considered for interpreting the data collected and hence facilitating the communication with the patient during interactive therapy session. This paper focuses on establishing a more adaptable and responsive rehabilitation process. The overarching objective of the system is to furnish a user centric, engaging, and collaborative rehabilitation journey for individuals with mobility disabilities.