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



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.


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.


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.


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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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.


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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Correspondence to Fabio Martínez.

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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).

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  • Gait analysis
  • Kinematic analysis
  • Dense trajectories
  • Parkinson’s disease