NDLI: Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

Content Provider	Springer Nature : BioMed Central
Author	Filtjens, Benjamin Ginis, Pieter Nieuwboer, Alice Slaets, Peter Vanrumste, Bart
Abstract	Background 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. Methods 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 proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. Results 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. Conclusions 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.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-022-01025-3.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-022-01025-3
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2022-05-21
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering Temporal convolutional neural networks Graph convolutional neural networks Freezing of gait Parkinson’s disease MS-GCN
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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