  Freezing of gait (FOG) is a common and debilitating gait impairment in
Parkinson's disease. Further insight into this phenomenon is hampered by the
difficulty to objectively assess FOG. To meet this clinical need, this paper
proposes an automated motion-capture-based FOG assessment method driven by a
novel deep neural network. Automated FOG assessment can be formulated as an
action segmentation problem, where temporal models are tasked to recognize and
temporally localize the FOG segments in untrimmed motion capture trials. This
paper takes a closer look at the performance of state-of-the-art action
segmentation models when tasked to automatically assess FOG. Furthermore, a
novel deep neural network architecture is proposed that aims to better capture
the spatial and temporal dependencies than the state-of-the-art baselines. The
proposed network, termed multi-stage spatial-temporal graph convolutional
network (MS-GCN), combines the spatial-temporal graph convolutional network
(ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The
ST-GCN captures the hierarchical spatial-temporal motion among the joints
inherent to motion capture, while the multi-stage component reduces
over-segmentation errors by refining the predictions over multiple stages. The
experiments indicate that the proposed model outperforms four state-of-the-art
baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an
excellent (r=0.93 [0.87, 0.97]) and moderately strong (r=0.75 [0.55, 0.87])
linear relationship with FOG outcomes derived from manual annotations. The
proposed MS-GCN may provide an automated and objective alternative to
labor-intensive clinician-based FOG assessment. Future work is now possible
that aims to assess the generalization of MS-GCN to a larger and more varied
verification cohort.
