Estimating Gait Parameters Using a Smart Walker With Human–Robot Interaction and Machine Learning Models

Abstract

The population of individuals with mobility impairments has been significantly increasing due to aging, diseases, or accidents. Gait analysis plays a crucial role in investigating musculoskeletal functions and gait parameters, enabling the assessment of mobility diseases, and providing valuable data for optimizing physical therapy treatments. Smart walkers can not only provide physical support to patients but also monitor the user’s walking gait. In this research, force sensors and built-in encoders are used to extract various gait parameters. Machine learning techniques, which have the ability to adapt to users with different gait patterns, are used to analyze users’ gait through interaction force. Three machine learning models, Support Vector Machine, Random Forest, and Neural Network are selected to perform gait analysis. Experiments with 5 subjects are conducted to evaluate the performance of the machine learning models in gait parameters estimation. Results from this pilot study show that the accuracies of the three machine learning models are all above 90% and the performance is much better than the traditional method and Random Forest model has slightly better accuracy among the small sample-size populations. In addition, SVM has the fastest response with 1.2 ms prediction time, which is suitable for online evaluation.