MATLAB-based tools for automated processing of motion tracking data provided by the GRAIL.

Area of Science:

• Biomechanics
• Human Movement Analysis
• Computational Science

Background:

• Bipedal locomotion is crucial for daily mobility, requiring coordinated muscle activation and potentially cognitive input.
• Objective gait assessment relies on tools like motion capture, force plates, and electromyography.
• The Gait Real-time Analysis Interactive Lab (GRAIL) offers standardized gait analysis but generates complex, multi-format data.

Purpose of the Study:

• To develop a user-friendly toolset for automated processing of motion capture data from the GRAIL system.
• To address the challenges of data handling, file formats, and sampling rates associated with GRAIL data.
• To facilitate more efficient gait analysis in experimental and clinical settings.

Main Methods:

• Development of a MATLAB-based toolset for automated data processing.
• Implementation of algorithms for reading, resampling, filtering, and synchronizing diverse data files.
• Inclusion of a coordinate-based algorithm for detecting gait events (initial contact, toe-off) and enabling gait cycle normalization.
• Incorporation of batch processing capabilities and automatic outlier detection.

Main Results:

• The toolset successfully automates the processing of motion capture data from the GRAIL.
• It handles various input file formats and sampling rates, standardizing the data.
• Automated detection of initial contact and toe-off allows for gait cycle segmentation and normalization.
• Batch analysis and outlier detection streamline the workflow for multiple datasets.

Conclusions:

• The developed MATLAB toolset simplifies and automates complex GRAIL gait analysis data processing.
• This toolset enhances efficiency for researchers and clinicians working with motion capture data.
• The authors encourage the research community to utilize and adapt the toolset for their studies.