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<p>Movement disorders are the main symptoms of neurological diseases such as Parkinson’s disease and deep learning-related methods can provide some intelligent solutions for the assessment and diagnosis of Parkinson’s movement disorders. In this paper, we propose a Kinect-based movement disorder assessment and analysis method, which uses the Kinect algorithm to capture and inverse kinematics analysis of human skeletal points, and further suggests the study of movement disorder assessment method based on dynamic time regularization algorithm so as to further achieve the effect of movement disorder assessment. Through the clinical experimental research on Parkinson’s disease patients and healthy subjects of the same age group, the use of the algorithm proposed in this paper is 15.18% higher than the GaitSet method in the CL state. The error of the algorithm proposed in this paper in the experiments comparing the gait parameter with the gold-standard motion capture system is close to 0.03s, which is a better improvement and upgrade compared with the advanced skeleton-based methods. In summary, the algorithm proposed in this paper is valuable and feasible for use in the assessment of Parkinson’s dyskinesia.</p>
</abstract>

Movement disorders are the main symptoms of neurological diseases such as Parkinson’s disease and deep learning-related methods can provide some intelligent solutions for the assessment and diagnosis of Parkinson’s movement disorders. In this paper, we propose a Kinect-based movement disorder assessment and analysis method, which uses the Kinect algorithm to capture and inverse kinematics analysis of human skeletal points, and further suggests the study of movement disorder assessment method based on dynamic time regularization algorithm so as to further achieve the effect of movement disorder assessment. Through the clinical experimental research on Parkinson’s disease patients and healthy subjects of the same age group, the use of the algorithm proposed in this paper is 15.18% higher than the GaitSet method in the CL state. The error of the algorithm proposed in this paper in the experiments comparing the gait parameter with the gold-standard motion capture system is close to 0.03s, which is a better improvement and upgrade compared with the advanced skeleton-based methods. In summary, the algorithm proposed in this paper is valuable and feasible for use in the assessment of Parkinson’s dyskinesia.

Deep learning in the assessment of movement disorders in Parkinson’s disease

Beijing Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals

School of Medical Technology, Beijing Institute of Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Movement disorders are the main symptoms of neurological diseases such as Parkinson’s disease and deep learning-related methods can provide some intelligent...

Deep learning in the assessment of movement disorders in Parkinson’s disease

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 22 mar 2024

Aceptado: 19 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1896

Palabras clave
Kinect algorithm, Dynamic time regularization, Deep learning, Movement disorder assessment, Parkinson’s disease

© 2024 Yumeng Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep learning in the assessment of movement disorders in Parkinson’s disease

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 22 mar 2024

Aceptado: 19 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1896

Palabras claveKinect algorithm, Dynamic time regularization, Deep learning, Movement disorder assessment, Parkinson’s disease

© 2024 Yumeng Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Kinect algorithm, Dynamic time regularization, Deep learning, Movement disorder assessment, Parkinson’s disease