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Quantification of Parkinsonian Kinematic Patterns in Body-Segment Regions During Locomotion

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Abstract

Purpose

Diagnosis and treatment of Parkinson’s Disease (PD) are typically supported by a kinematic gait analysis. Nonetheless, the main drawbacks of the classical analysis, based on a reduced set of markers, are the loss of small dynamical changes, the invasive methodology, and the sparse representation from few points, restricting the disease analysis. This work aims to perform a robust regional kinematic characterization, which may result in a potential digital biomarker of the disease to complement personalized analysis, treatment and monitoring of PD.

Methods

This work introduces a markerless computational framework based on a full body-segment kinematic characterization related with PD motor alterations. Firstly, a set of dense motion trajectories are computed to represent locomotion. Such trajectories are grouped using a deep learning based body segmentation, that partitions the human silhouette into regions corresponding to the head, trunk and limbs. Each resultant region is described using dartboard-like kinematic histograms computed along the trajectories.

Results

The proposed approach was validated using different pretrained classification models. The proposed method was evaluated on a set of 11 control subjects and 11 PD patients, achieving an average accuracy of \(99.62\%\) for lower-limbs and head regions.

Conclusion

This work proved to be effective to classify Parkinsonian patterns w.r.t control gaits. A major contribution of the proposed strategy is the capability to recover kinematic patterns in different body segments, particularly, for head and trunk regions, which turned out to be a decisive PD biomarker.

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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 for their willingness to collaborate in this research by opening their doors. Additionally, support of the Vicerrectoría de Investigación y Extensión of the Universidad Industrial de Santander is gratefully acknowledged, through the funding of the project: Cuantificación de patrones locomotores para el diagnóstico y seguimiento remoto en zonas de dificil acceso, with SIVIE code 2697.

Funding

The Vicerrectoría de Investigación y Extensión of the Universidad Industrial de Santander supported this research registered by the project: “Cuantificación de patrones locomotores para el diagnóstico y seguimiento remoto en zonas de difícil acceso”, with SIVIE code 2697.

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Authors and Affiliations

  1. Biomedical Imaging, Vision and Learning Laboratory (BivL2ab), Universidad Industrial de Santander, Ciudad Universitaria, Bucaramanga, 680002, Santander, Colombia

    Luis C. Guayacán & Fabio Martínez

  2. Unité d’Informatique et d’Ingénierie des Systèmes (U2IS), ENSTA Paris, Institut Polytechnique de Paris, 91762, Palaiseau, Ile de France, France

    Antoine Manzanera

Authors
  1. Luis C. Guayacán
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  2. Antoine Manzanera
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  3. Fabio Martínez
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Correspondence to Fabio Martínez.

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Ethical Approval

This study was approved by the Ethical Committee of the Universidad Industrial de Santander (session held on July 19th, 2019) and counts on a written informed consent.

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Guayacán, L.C., Manzanera, A. & Martínez, F. Quantification of Parkinsonian Kinematic Patterns in Body-Segment Regions During Locomotion. J. Med. Biol. Eng. 42, 204–215 (2022). https://doi.org/10.1007/s40846-022-00691-x

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  • DOI: https://doi.org/10.1007/s40846-022-00691-x

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