Automated freezing of gait assessment with marker-based motion capture and deep learning approaches expert-level detection

• URL: http://arxiv.org/abs/2103.15449v1
• Date: Mon, 29 Mar 2021 09:32:45 GMT
• Title: Automated freezing of gait assessment with marker-based motion capture and deep learning approaches expert-level detection
• Authors: Benjamin Filtjens, Pieter Ginis, Alice Nieuwboer, Peter Slaets, and Bart Vanrumste
• Abstract summary: This paper proposes a motion capture-based FOG assessment method driven by a novel deep neural network. The proposed network, termed multi-stage graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN) Experiments indicate that the proposed model outperforms state-of-the-art baselines.
• Score: 0.9103175498038585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight in this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes a motion capture-based FOG assessment method driven by a novel deep neural network. The proposed network, termed multi-stage 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 motion among the optical markers 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. The experiments indicate that the proposed model outperforms state-of-the-art baselines. An in-depth quantitative and qualitative analysis demonstrates that the proposed model is able to achieve clinician-like FOG assessment. The proposed MS-GCN can provide an automated and objective alternative to labor-intensive clinician-based FOG assessment.

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