3D Canonical Pose Estimation and Abnormal Gait Recognition with a Single RGB-D Camera

Yao Guo, Fani Deligianni, Xiao Gu, Guang-Zhong Yang
2019 IEEE Robotics and Automation Letters  
Assistive robots play an important role in improving the quality of life of patients at home. Among all the monitoring tasks, gait disorders are prevalent in elderly and people with neurological conditions, which increases the risk of fall. Therefore, the development of mobile systems for gait monitoring at home in normal living conditions is important. Here we present a mobile system that is able to track humans and analyze their gait in canonical coordinates based on a single RGB-D camera.
more » ... stly, view-invariant 3D lower limb pose estimation is achieved by fusing information from depth images along with 2D joints derived in RGB images. Next, both the 6D camera pose and the 3D lower limb skeleton are real-time tracked in a canonical coordinate system based on Simultaneously Localization and Mapping (SLAM). A maskbased strategy is exploited to improve the re-localization of the SLAM in dynamic environments. Abnormal gait is detected by using the Support Vector Machine (SVM) and the Bidirectional Long-Short Term Memory (BiLSTM) network with respect to a set of extracted gait features. To evaluate the robustness of the system, we collected multi-camera, ground truth data from sixteen healthy volunteers performing six gait patterns that mimic common gait abnormalities. The experiment results demonstrate that our proposed system can achieve good lower limb pose estimation and superior recognition accuracy compared to previous abnormal gait detection methods. Recent advances in computer vision have demonstrated good performance in offline markerless gait analysis [5]-
doi:10.1109/lra.2019.2928775 fatcat:4etczszm35hjnigssyn3ft5oaq