Post-stroke rehabilitation optimization & recommendation framework using tele-robotic ecosystem: Industry 4.0 readiness approach

Abstract

Technological development in biomedical procedures has given an upper understanding of the ease of evaluating and handling critical scenarios and diseases. A sustainable model design is required for the post-medical procedures to maintain the consistency of medical treatment. In this article, a telerobotic-based stroke rehabilitation optimization and recommendation technique cum framework is proposed and evaluated. Selecting optimal features for training deep neural networks can help in optimizing the training time and also improve the performance of the model. To achieve this, we have used Whale Optimization Algorithm (WOA) due to its higher convergence accuracy, better stability, stronger global search ability, and faster convergence speed to streamline the dependency matrix of each attribute associated with post-stroke rehabilitation. Deep Neural Networking assures the selection of datasets from training and testing validation. The proposed framework is developed on providing decision support with a recommendation of activities and task flow, these recommendations are independent and have higher feasibility with the scenario of evaluation. The proposed model achieved a precision of 99.6%, recall of 99.5 %, F1-score of 99.7%, and accuracy of 99.9%, which outperform the other considered optimization algorithms such as antlion and gravitational search algorithms. The proposed technique has provided an efficient recommendation model compared to the trivial SVM-based models and techniques.