Adaptive, AI-based automated knee physiotherapy system

The paper is introduced with a brief survey about existing causes for Knee ailments followed by conventional treatments for them. Studies show that ailments like Osteo Arthritis (OA) of the Knee and Knee related injuries cause chronic pain and stiffness to the knee joint. This affects the range of motion of the leg. The severity of this is highly dependent on the age and BMI (Body-mass Index) of the patient. Further, a contrast between conventional Physiotherapy Machines (CPM) and the proposed model is established. The paper proposes an alternative to the existing CPMs. A cost-effective system capable of diagnosing the severity of the knee using machine learning models and provide appropriate Automated physiotherapy. Using gyroscopic data and a predefined questionnaire, a 1D-CNN is trained. An accuracy of 90.21% was obtained from the machine learning model. The accuracy of the proposed model exceeded the accuracy of some state-of-the-art algorithms in determining the severity of the affected knee by utilizing gyroscopic parameters and with the least computational cost.

© 2019 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.