Objective: We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson’s disease using a computer vision-based methodology.

Background: Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease. In the clinic, gait impairment is commonly rated as part of the  Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS [1]) assessment (item 3.10). The system we developed can be used to obtain an objective (second) opinion for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician; for example during remote home assessments.

Method: Videos (n=534) were collected as part of routine MDS-UPDRS gait assessments of Parkinson’s patients, and the deep learning library OpenPose [2] was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician (0, 1, 2, or 3; the data did not include severity 4 which would mean the patient is unable to walk). Eight features were calculated from time-series signals of the extracted key-points. These features characterised key aspects of the movement including step frequency (estimated using a novel Gamma-Poisson Bayesian model), stride width, arm movement, acceleration, and left-right asymmetry. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation.
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Results: Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 600 video clips showing patients walking towards or away from the camera (Pearson’s r=0.82, p<0.001). Our classifier achieved a balanced accuracy of 55% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians’ ratings in 93% of cases.

Conclusion: The severity of gait impairment in Parkinson’s disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e. videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective second opinion of the rating, and has the potential to be used for remote home assessments.

References: [1] Goetz, Christopher G., et al. “Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results.” Movement disorders: official journal of the Movement Disorder Society 23.15 (2008): 2129-2170. [2] Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2019). OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. IEEE transactions on pattern analysis and machine intelligence, 43(1), 172-186.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/a-computer-vision-based-method-for-quantifying-parkinsonian-gait/