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A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification

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Abstract

Gait is one of the most important biomarkers for Parkinson’s disease (PD). Nonetheless, current clinical diagnosis to quantify locomotion patterns uses coarse approximations, from a set of reduced marker-based trajectories. This approximation, among others, results restrictive, invasive, alters natural gait gestures, and leaves out relevant PD patterns. This paper introduces a new computational approach to quantify, classify and explain Parkinson gait patterns using a markerless video strategy. The core of the work is a local volumetric covariance to codify motion patterns during locomotion. Such covariance codifies convolutional pre-trained features tracked along a set of dense trajectories which represent subject’s gait. Covariance pattern computation involves an integral strategy to remain efficient in terms of computational cost. The proposed method was evaluated on 176 gait video sequences of a total of 22 patients among control and diagnosed with PD. The proposed approach achieved a remarkable average accuracy of 96.59% (± 0.13) with a sensitivity of 98.86%, specificity of 94.31%, and precision of 94.56%. These results suggest that the proposed approach may support clinical PD diagnosis and analysis using ordinary videos.

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  1. https://github.com/oskkr123/VolumetricCov-descriptor

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Acknowledgements

The authors thank the FAMPAS Foundation (Fundación del Adulto Mayor y Parkinson Santander) and the nursing home Asilo San Rafael. Additionally, gratefulacknowledgments to the Vicerrectoría de Investigación y Extensión of the Universidad Industrial de Santander for supporting this research registered by theproject: Cuantificación de patrones locomotores para el diagnóstico y seguimiento remoto en zonas de dificil acceso, with SIVIE code 2697.

Funding

This work was partially funded by the Vicerrectoría de Investigación y Extensión of Universidad Industrial de Santander with the project: Cuantificación de patrones locomotores para el diagnóstico y seguimiento remoto en zonas de dificil acceso, with SIVIE code 2697.

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  1. Biomedical Imaging, Vision and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander (UIS), Bucaramanga, Colombia

    Oscar Mendoza, Fabio Martínez & Juan Olmos

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  1. Oscar Mendoza
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  2. Fabio Martínez
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  3. Juan Olmos
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Correspondence to Fabio Martínez.

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Mendoza, O., Martínez, F. & Olmos, J. A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification. Multimed Tools Appl 81, 30733–30748 (2022). https://doi.org/10.1007/s11042-022-12280-w

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