  Musculoskeletal and neurological disorders are the most common causes of
walking problems among older people, and they often lead to diminished quality
of life. Analyzing walking motion data manually requires trained professionals
and the evaluations may not always be objective. To facilitate early diagnosis,
recent deep learning-based methods have shown promising results for automated
analysis, which can discover patterns that have not been found in traditional
machine learning methods. We observe that existing work mostly applies deep
learning on individual joint features such as the time series of joint
positions. Due to the challenge of discovering inter-joint features such as the
distance between feet (i.e. the stride width) from generally smaller-scale
medical datasets, these methods usually perform sub-optimally. As a result, we
propose a solution that explicitly takes both individual joint features and
inter-joint features as input, relieving the system from the need of
discovering more complicated features from small data. Due to the distinctive
nature of the two types of features, we introduce a two-stream framework, with
one stream learning from the time series of joint position and the other from
the time series of relative joint displacement. We further develop a mid-layer
fusion module to combine the discovered patterns in these two streams for
diagnosis, which results in a complementary representation of the data for
better prediction performance. We validate our system with a benchmark dataset
of 3D skeleton motion that involves 45 patients with musculoskeletal and
neurological disorders, and achieve a prediction accuracy of 95.56%,
outperforming state-of-the-art methods.
