Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

• Published in: IAES International Journal of Artificial Intelligence
• Main Author: Izhar C.A.A.; Hussain Z.; Maruzuki M.I.F.; Sulaiman M.S.; Rahim A.A.A.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114322989&doi=10.11591%2fijai.v10.i3.pp752-763&partnerID=40&md5=17f40879ac4be094ae0ce2a77f5c4ee7

The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle. © 2021, Institute of Advanced Engineering and Science. All rights reserved.