3-D Canonical pose estimation and abnormal gait recognition with a single RGB-D camera - Guo,Y; Deligianni,F; Gu,X; Yang,G-Z (IEEE Robotics and Automation Letters, 2019)
Assistive robots play an important role in improvingthe quality of life of patients at home. Among all the monitoringtasks, gait disorders are prevalent in elderly and people with neurological conditions and this increases the risk of fall. Therefore,the development of mobile systems for gait monitoring at home innormal living conditions is important. Here, we present a mobilesystem that is able to track humans and analyze their gait incanonical coordinates based on a single RGB-D camera. First,view-invariant three-dimensional (3-D) lower limb pose estimationis achieved by fusing information from depth images along with2-D joints derived in RGB images. Next, both the 6-D camerapose and the 3-D lower limb skeleton are real-time tracked in acanonical coordinate system based on simultaneously localizationand mapping (SLAM). A mask-based strategy is exploited to improve the re-localization of the SLAM in dynamic environments.Abnormal gait is detected by using the support vector machine andthe bidirectional long-short term memory network with respect toa set of extracted gait features. To evaluate the robustness of thesystem, we collected multi-cameras, ground truth data from 16healthy volunteers performing 6 gait patterns that mimic commongait abnormalities. The experiment results demonstrate that ourproposed system can achieve good lower limb pose estimation andsuperior recognition accuracy compared to previous abnormal gaitdetection methods.