Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—In this paper, a limb rehabilitation system based on the integration of Augmented Reality (AR) and Artificial Intelligence (AI) technology, which is called RAA, is introduced. By creating rehabilitation exercises on a physical system with a crank and pedal mechanism with an AR interface, the RAA enhances the patient’s incentive to exercise. Therefore, the efficacy of practice is increased. In addition, the AI platform serves as a medical assistant for patients by suggesting practicing courses according to Fuzzy logic. Furthermore, using the Perceptron network and Microsoft Kinect, the RAA can assess and correct a patient’s exercise posture, thereby decreasing reliance on physiotherapists. Training data is stored by the system during exercise, which is a critical parameter for analyzing and assessing recovery. Two experimental processes (n = 20) were conducted to assess the efficacy of the RAA, yielding promising results.
Index Terms—stroke, hemiparesis, rehabilitation, augmented reality, artificial intelligence
Cite: Nguyen Anh Quoc, Nguyen Vo Tam Toan, Tran The Luc, and Nguyen Truong Thinh, "A Limb Rehabilitation Training System Based on Augmented Reality and Artificial Intelligence," International Journal of Mechanical Engineering and Robotics Research, Vol. 11, No. 6, pp. 445-451, June 2022. DOI: 10.18178/ijmerr.11.6.445-451
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