  The aim of this study is developing an automatic system for detection of
gait-related health problems using Deep Neural Networks (DNNs). The proposed
system takes a video of patients as the input and estimates their 3D body pose
using a DNN based method. Our code is publicly available at
https://github.com/rmehrizi/multi-view-pose-estimation. The resulting 3D body
pose time series are then analyzed in a classifier, which classifies input gait
videos into four different groups including Healthy, with Parkinsons disease,
Post Stroke patient, and with orthopedic problems. The proposed system removes
the requirement of complex and heavy equipment and large laboratory space, and
makes the system practical for home use. Moreover, it does not need domain
knowledge for feature engineering since it is capable of extracting semantic
and high level features from the input data. The experimental results showed
the classification accuracy of 56% to 96% for different groups. Furthermore,
only 1 out of 25 healthy subjects were misclassified (False positive), and only
1 out of 70 patients were classified as a healthy subject (False negative).
This study presents a starting point toward a powerful tool for automatic
classification of gait disorders and can be used as a basis for future
applications of Deep Learning in clinical gait analysis. Since the system uses
digital cameras as the only required equipment, it can be employed in domestic
environment of patients and elderly people for consistent gait monitoring and
early detection of gait alterations.
