Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture

Abstract

The present study investigates the effectiveness of an integrated approach that combines machine learning, robotic rehabilitation, and traditional methods for wrist rehabilitation among the hemiparesis population. Subjects with robotic rehabilitation at the four-week intervention period showed the ball’s significant difference in the flexion and extension angles and demonstrated the better overall outcomes from the robotic intervention . The machine learning models, in particular, the artificial neural networks, provided the prediction of rehabilitation outcomes and demonstrated the high accuracy, high precision, high recall, and high F1 score. This study supports the idea of the beneficial effects of combining robotic rehabilitation with machine learning algorithms and supports the notion that the two benefits could bring high synergistic effects above their individual potentials. The report also contributes to the development of the rehabilitation science of wrists, given that few studies have explored the applications of artificial intelligence methods or robotic rehabilitation beyond the context of feasibility reports. The findings of the current research could contribute useful insights for the decision-making practices of therapists and healthcare providers, also given the reliance on large data contributed by the previous interventions. Future study could investigate the long-term results, the cost-effectiveness of the approaches, and assist in developing the potential results of customization for individual patients.