Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling

Abstract

Foot motion recognition in daily life faces two challenges imposed by traditional machine learning frameworks: how to robustly recognize various foot motions from continuous movements in uncontrolled environments, and how to accurately extract ground truths. To address these challenges, in this paper, we propose a stride detection method to robustly identify each stride (over 99% accuracy). We then investigate two weak supervision-based fast labeling frameworks to automatically label the stride segmentations. Finally, we use these two frameworks to identify foot motions from continuous movements integrated on a route map. The route map can be replaced by a virtual-reality video game to play in daily life so that the user's long-term foot functionality can be profiled and evaluated. We test our proposed approaches using a smart insole with twenty-two subjects whose movement data are collected through the route map setting while video camera recordings serve as the ground truth. The route map integrates seven foot motions in one complete play, which includes three continuous motions resulted from continuous full-body movements (named as continuous motions) and four intermittent foot motions that are produced repetitively (named as repetitive motions). Compared to the best traditional machine learning methods, our proposed approach improves the leave-one-subject-out cross-validation accuracy of all subjects by 6.12% for the three continuous motions, 2.71% for the four repetitive motions and 4.90% for the total of seven foot motions. In addition, our proposed method saves 25% to 50% time in data labeling.