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 and this 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. First,
view-invariant three-dimensional (3-D) lower limb pose estimation
is achieved by fusing information from depth images along with
2-D joints derived in RGB images. Next, both the 6-D camera
pose and the 3-D lower limb skeleton are real-time tracked in a
canonical coordinate system based on simultaneously localization
and 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 and
the bidirectional long-short term memory network with respect to
a set of extracted gait features. To evaluate the robustness of the
system, we collected multi-cameras, ground truth data from 16
healthy volunteers performing 6 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.