If your doctor and therapists can't figure out how to use this to objectively diagnose your gait problems and then assign EXACT REHAB PROTOCOLS to fix them, you need better doctors and therapists.
Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks
Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 48 (2022)
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.
Background
Freezing of gait (FOG) is a common and debilitating gait impairment of Parkinson’s disease (PD). Up to 80% of people with Parkinson’s disease (PwPD) may develop FOG during the course of the disease [1, 2]. FOG leads to sudden blocks in walking and is clinically defined as a “brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk and reach a destination” [3]. The PwPD themselves describe freezing of gait as “the feeling that their feet are glued to the ground” [4]. Freezing episodes most frequently occur while traversing under environmental constraints, during emotional stress, during cognitive overload by means of dual-tasking, and when initiating gait [5, 6]. Though, turning hesitation was found to be the most frequent trigger of FOG [7, 8]. Subjects with FOG experience more anxiety [9], have a lower quality of life [10], and are at a much higher risk of falls [11,12,13,14,15].
Given the severe adverse effects associated with FOG, there is a large incentive to advance novel interventions for FOG [16]. Unfortunately, the pathophysiology of FOG is complex and the development of novel treatments is severely limited by the difficulty to objectively assess FOG [17]. Due to heightened levels of attention, it is difficult to elicit FOG in the gait laboratory or clinical setting [4, 6]. Therefore, health professionals relied on subjects’ answers to subjective self-assessment questionnaires [18, 19], which may be insufficiently reliable to detect FOG severity [20]. Visual analysis of regular RGB videos has been put forward as the gold standard for rating FOG severity [20, 21]. However, the visual analysis relies on labor-intensive manual annotation by a trained clinical expert. As a result, there is a clear need for an automated and objective approach to assess FOG.
The percentage time spent frozen (%TF), defined as the cumulative duration of all FOG episodes divided by the total duration of the walking task, and the number of FOG episodes (#FOG) have been put forward as reliable outcome measures to objectively assess FOG [22]. An accurate segmentation in-time of the FOG episodes, with minimal over-segmentation errors, is required to robustly determine both outcome measures.
Several methods have been proposed for automated FOG assessment based on motion capture (MoCap) data. MoCap encodes human movement as a time series of human joint locations and orientations or their higher-order representations and is typically performed with optical or inertial measurement systems. Prior work has tackled automated FOG assessment as an action recognition problem and used a sliding-window scheme to segment a MoCap sequence into fixed partitions [23,24,25,26,27,28,29,30,31,32,33,34,35,36]. For all the samples within a partition, a single label is then predicted with methods ranging from simple thresholding methods [23, 26] to high-level temporal models driven by deep learning [27, 30, 32, 33, 36]. However, the samples within a pre-defined partition may not always share the same label. Therefore, a data-dependent heuristic is imposed to force all samples to take a single label, most commonly by majority voting [33, 36]. Moreover, a second data-dependent heuristic is needed to define the duration of the sliding-window, which is a trade-off between expressivity, i.e., the ability to capture long-term temporal patterns, and sensitivity, i.e., the ability to identify short-duration FOG episodes. Such manually defined heuristics are unlikely to generalize across study protocols.
This study proposes to reformulate the problem of FOG annotation as an action segmentation problem. Action segmentation approaches overcome the need for manually defined heuristics by generating a prediction for each sample within a long untrimmed MoCap sequence. Several methods have been proposed to tackle action segmentation. Similar to FOG assessment, earlier studies made use of sliding-window classifiers [37, 38], which do not capture long-term temporal patterns [39]. Other approaches use temporal models such as hidden Markov models [40, 41] and recurrent neural networks [42, 43]. The state-of-the-art methods tend to use temporal convolutional neural networks (TCN), which have been shown to outperform recurrent methods [39, 44]. Dilation is frequently added to capture long-term temporal patterns by expanding the temporal receptive field of the TCN models [45]. In multi-stage temporal convolutional network (MS-TCN), the authors show that multiple stages of temporal dilated convolutions significantly reduce over-segmentation errors [46]. These action segmentation methods have historically been validated on video-based datasets [47, 48] and thus employ video-based features [49]. The human skeleton structure that is inherent to MoCap has thus not been exploited by prior work in action segmentation.
To model the structured information among the markers, this paper uses the spatial-temporal graph convolutional neural network (ST-GCN) [50] as the first stage of an MS-TCN network. ST-GCN applies spatial graph convolutions on the human skeleton graph at each time step and applies dilated temporal convolutions on the temporal edges that connect the same markers across consecutive time steps. The proposed model, termed multi-stage spatial-temporal graph convolutional neural network (MS-GCN), thus extends MS-TCN to skeleton-based data for enhanced action segmentation within MoCap sequences.
The MS-GCN was tasked to recognize and localize FOG segments in a MoCap sequence. The predicted segments were quantitatively and qualitatively assessed versus the agreed-upon annotations by two clinical-expert raters. From the predicted segments, two clinically relevant FOG outcomes, the %TF and #FOG, were computed and statistically validated. To the best of our knowledge, the proposed MS-GCN is a novel neural network architecture for skeleton-based action segmentation in general and FOG segmentation in particular. The benefit of MS-GCN for FOG assessment is four-fold: (1) It exploits ST-GCN to model the structured information inherent to MoCap. (2) It allows modeling of long-term temporal context to capture the complex dynamics that precede and succeed FOG. (3) It can operate on high temporal resolutions for fine-grained FOG segmentation with precise temporal boundaries. (4) To accomplish (2) and (3) with minimal over-segmentation errors, MS-GCN utilizes multiple stages of refinement.
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