Experimental Evaluation of Machine Learning Models for Gait Segmentation

Accurate estimation of the gait phase is extremely important in exoskeleton control, as various stages in the gait cycle require different control objectives. This study involved the evaluation of seven machine learning models utilizing inertial measurement unit and joint angle data from eight healthy subjects wearing the Ekso Indego exoskeleton for the purpose of gait segmentation. During the experiments, subjects walked on a level instrumented treadmill with gravity compensation assistance for three trials and underwent a fourth trial simulating impairment. A six-state model of gait was employed, where bilateral heel strike, toe off, and tibia vertical determined state transitions. True state transitions were determined using optical motion capture and ground reaction force data. Of the evaluated models, the Support Vector Machine achieved the highest performance, with an average accuracy of 94.5% and 94.1% for normal walking and impaired walking, respectively. Future research should focus on assessing the model's real-time performance among exoskeleton users before considering its application as the basis for an exoskeleton controller.