Using Plantar Pressure and Machine Learning to Automatically Evaluate Strephenopodia for Rehabilitation Exoskeleton: A Pilot Study

Stroke patients often suffer from strephenopodia, which seriously affects their walking ability and rehabilitation. However, lower limb rehabilitation robots lack the evaluation and automatic correction function of strephenopodia. There are practical demands for convenient, automatic, and quantitative assessments of the angle of strephenopodia to adjust the orthopedic strength in time to remind stroke patients to use their muscles to realize the movements. In this study, we proposed a novel methodology for automatically predicting the angles of strephenopodia based on a plantar pressure system using machine learning methods. Three machine learning methods were implemented to build stochastic function mapping from gait features to strephenopodia angles, showing good reliability and precision prediction of the strephenopodia angle [determination coefficient (R2) ≥ 0.80]. Results showed that our method is convenient to implement and outperforms previous methods in accuracy. Therefore, measurements derived from the plantar pressure system are proper estimators of the strephenopodia angle and are beneficial to lower limb rehabilitation exoskeleton for stroke population training.