MATLAB-based tools for automated processing of motion tracking data provided by the GRAIL.
Batch processing
Biomechanics
GRAIL
Gait analysis
Matlab toolbox
Synchronization of source files
Journal
Gait & posture
ISSN: 1879-2219
Titre abrégé: Gait Posture
Pays: England
ID NLM: 9416830
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
received:
11
04
2021
revised:
08
09
2021
accepted:
14
09
2021
pubmed:
2
10
2021
medline:
15
12
2021
entrez:
1
10
2021
Statut:
ppublish
Résumé
The ability for independent bipedal locomotion is an important prerequisite for autonomous mobility and participation in everyday life. Walking requires not only a functional musculoskeletal unit but relies on coordinated activation of muscles and may even require cognitive resources. The time-resolved monitoring of the position of joints, feet, legs and other body segments relative to each other alone or in combination with simultaneous recording of ground reaction forces and concurrent measurement of electrical muscle activity, using surface electromyography, are well-established tools for the objective assessment of gait. The Gait Real-time Analysis Interactive Lab (GRAIL) has been introduced for gait analysis in a highly standardized and well-controlled virtual environment. However, apart from high computing capacity and sophisticated software required to run the system, handling of GRAIL data is challenging due to the utilization of different software packages resulting in a huge amount of data stored using different file formats and different sampling rates. These issues make gait analysis even with such a sophisticated instrument rather tedious, especially within the frame of an experimental or clinical study. A user-friendly Matlab based toolset for automated processing of motion capturing data recorded using the GRAIL, with the inherent option for batch analysis was developed. The toolset allows the reading, resampling, filtering and synchronization of data stored in different input files recorded with the GRAIL. It includes a coordinate-based algorithm for the detection of initial contact and toe-off events to split and normalize data relative to gait cycles. Batch processing of multiple measurements and automatic detection of outliers is possible. The authors hope that the toolset will be useful to the research community and invite everyone to use, modify or implement it in their own work.
Sections du résumé
BACKGROUND
The ability for independent bipedal locomotion is an important prerequisite for autonomous mobility and participation in everyday life. Walking requires not only a functional musculoskeletal unit but relies on coordinated activation of muscles and may even require cognitive resources. The time-resolved monitoring of the position of joints, feet, legs and other body segments relative to each other alone or in combination with simultaneous recording of ground reaction forces and concurrent measurement of electrical muscle activity, using surface electromyography, are well-established tools for the objective assessment of gait.
RESEARCH QUESTION
The Gait Real-time Analysis Interactive Lab (GRAIL) has been introduced for gait analysis in a highly standardized and well-controlled virtual environment. However, apart from high computing capacity and sophisticated software required to run the system, handling of GRAIL data is challenging due to the utilization of different software packages resulting in a huge amount of data stored using different file formats and different sampling rates. These issues make gait analysis even with such a sophisticated instrument rather tedious, especially within the frame of an experimental or clinical study.
METHODS
A user-friendly Matlab based toolset for automated processing of motion capturing data recorded using the GRAIL, with the inherent option for batch analysis was developed.
RESULTS
The toolset allows the reading, resampling, filtering and synchronization of data stored in different input files recorded with the GRAIL. It includes a coordinate-based algorithm for the detection of initial contact and toe-off events to split and normalize data relative to gait cycles. Batch processing of multiple measurements and automatic detection of outliers is possible.
SIGNIFICANCE
The authors hope that the toolset will be useful to the research community and invite everyone to use, modify or implement it in their own work.
Identifiants
pubmed: 34597983
pii: S0966-6362(21)00489-6
doi: 10.1016/j.gaitpost.2021.09.179
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
422-426Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.