  With the increasing awareness of high-quality life, there is a growing need
for health monitoring devices running robust algorithms in home environment.
Health monitoring technologies enable real-time analysis of users' health
status, offering long-term healthcare support and reducing hospitalization
time. The purpose of this work is twofold, the software focuses on the analysis
of gait, which is widely adopted for joint correction and assessing any lower
limb or spinal problem. On the hardware side, we design a novel marker-less
gait analysis device using a low-cost RGB camera mounted on a mobile
tele-robot. As gait analysis with a single camera is much more challenging
compared to previous works utilizing multi-cameras, a RGB-D camera or wearable
sensors, we propose using vision-based human pose estimation approaches. More
specifically, based on the output of two state-of-the-art human pose estimation
models (Openpose and VNect), we devise measurements for four bespoke gait
parameters: inversion/eversion, dorsiflexion/plantarflexion, ankle and foot
progression angles. We thereby classify walking patterns into normal,
supination, pronation and limp. We also illustrate how to run the purposed
machine learning models in low-resource environments such as a single
entry-level CPU. Experiments show that our single RGB camera method achieves
competitive performance compared to state-of-the-art methods based on depth
cameras or multi-camera motion capture system, at smaller hardware costs.
