An Empirical Review of Supervised and Reinforcement Learning Algorithms for Personalized Exoskeleton Robot Training Systems in Neurorehabilitation

There is an increasing need for developing more personalized machine learning algorithms for the training system of exoskeleton robots that are in use for the purpose of human neurorehabilitation patients. With increased neurological disorders, demand for effective and individually tailored rehabilitation solutions parallels. Traditional methods of rehabilitation are inadequate to keep up with the adjustment of treatment based on the needs of a patient that may result in very poor recuperation results. Current reviews on the applicability of machine learning in neurorehabilitation rarely fully consider the needs and complexities involved in the design of personalized exoskeleton training systems. There are several major limitations in the literature reviews: very often, they do not consider the possibility of integrating machine learning personalization into the processes of exoskeleton training; besides, in most cases, they lack a methodical way to compare algorithm effectiveness in various patient profiles; lastly, they do not give an adequate meta-analysis of the strong and weak points for the existing methodologies. These gaps impede the construction of more effective and more individualized rehabilitation programs. In this respect, the systematic review that will be presented herein will involve a systematic literature review, meta-analysis, and critical appraisal following machine learning algorithms specific for exoskeleton robot training systems. The methods include a systematic literature review by searching, categorizing, and the relevant studies; then meta-analysis is carried out based on the data gathered to give a quantitative assessment of the performance and effectiveness of these algorithms. This review is guided by the PRISMA guidelines for rigorous and transparent reporting. Furthermore, a critical appraisal framework enhances the review so that strengths and weaknesses of the many different machine learning techniques—from supervised learning to reinforcement learning and deep learning models—may be identified and appraised. This review process aims at the following: it outlines procedures for a systematic and comprehensive assessment of personalized ML algorithms and responses to major shortcomings, and as such, identifies new ideas, methods, and solutions potentially useful for further research. The purpose of this work is to follow on the existing constraints systematically and provide an in-depth understanding of the performance of the various algorithms to fully enhance the development on personalized neurorehabilitation solutions. This reviewer's contribution will thus have an effect on improved patient outcomes, optimize rehabilitation processes, and chart a course for future research toward ever more effective and personalized exoskeleton training systems