Data Standardization for Gait Analysis in the Oritel Network

Gait, a complex process unique to everyone, has been extensively studied in fields such as medicine, rehabilitation, and biomechanics. Gait laboratories, like those at Teletón centers, play a crucial role in analyzing and diagnosing human movement patterns during locomotion. This report focuses on standardizing data from different gait laboratories. This initiative addresses the lack of uniformity, which hinders the comparison and exchange of information between different rehabilitation centers. The proposal is part of the Movement Analysis Network project, an initiative by Politecnico di Milano, Oritel, and Universidad de Concepción, promoting collaboration, research, and continuous improvement in movement analysis. The proposed methodology particularly focuses on standardizing data acquired from BTS GaitLab and Vicon Systems. To achieve an effective comparison between data from these two systems, it is essential to define uniform labels and automate the processes that allow for the comparison of the information contained in the files. For this purpose, our script takes C3D and EMT files as input and generates table-formatted files separated by study type as output. Then, those files are used as input for a NoSQL database. This database facilitates further data comparison. This work enables the creation of standardized databases, which can be used to improve the effectiveness and reliability of diagnoses and treatments related to gait pathologies. In a future work, machine learning techniques could be used for gait pattern classification, creating new opportunities for more precise and personalized medical care, thereby positively impacting patients' quality of life by tailoring treatments to their specific needs.

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Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1109/3dahm62677.2024.10920827
Primary Topic
Gait Recognition and Analysis
Type
article
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Data Standardization for Gait Analysis in the Oritel Network

Mónica Morante, Manuela Galli, Esteban J. Pino, Lorena A. Llorente et al.
Gait Recognition and Analysis
article

Data Standardization for Gait Analysis in the Oritel Network

Mónica Morante, Manuela Galli, Esteban J. Pino, Lorena A. Llorente, Rosario Ulloa
article en

Abstract

Gait, a complex process unique to everyone, has been extensively studied in fields such as medicine, rehabilitation, and biomechanics. Gait laboratories, like those at Teletón centers, play a crucial role in analyzing and diagnosing human movement patterns during locomotion. This report focuses on standardizing data from different gait laboratories. This initiative addresses the lack of uniformity, which hinders the comparison and exchange of information between different rehabilitation centers. The proposal is part of the Movement Analysis Network project, an initiative by Politecnico di Milano, Oritel, and Universidad de Concepción, promoting collaboration, research, and continuous improvement in movement analysis. The proposed methodology particularly focuses on standardizing data acquired from BTS GaitLab and Vicon Systems. To achieve an effective comparison between data from these two systems, it is essential to define uniform labels and automate the processes that allow for the comparison of the information contained in the files. For this purpose, our script takes C3D and EMT files as input and generates table-formatted files separated by study type as output. Then, those files are used as input for a NoSQL database. This database facilitates further data comparison. This work enables the creation of standardized databases, which can be used to improve the effectiveness and reliability of diagnoses and treatments related to gait pathologies. In a future work, machine learning techniques could be used for gait pattern classification, creating new opportunities for more precise and personalized medical care, thereby positively impacting patients' quality of life by tailoring treatments to their specific needs.

Politecnico di Milano (IT)
Openalex Percentile: Top 75%
Gait Recognition and Analysis
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