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Applying common filtering processes to Global Navigation Satellite System-derived acceleration during team sport
Robert I M Delves1, Grant M Duthie2, Kevin A Ball1
1Institute for Health and Sport, Victoria University, Melbourne, Victoria, Australia.
Journal of Sports Sciences
|March 14, 2022
Summary
This study found no significant differences in team-sport speed between Global Navigation Satellite System (GNSS) manufacturers after filtering. However, raw acceleration data showed differences, which were minimized by applying a standardized filter.
Area of Science:
- Sports Science
- Biomechanics
- Performance Analysis
Background:
- Global Navigation Satellite System (GNSS) devices are widely used in team sports for performance monitoring.
- Variations in data processing, including filtering, may influence the accuracy of GNSS-derived metrics.
- Understanding manufacturer-specific differences in acceleration and speed data is crucial for reliable athlete monitoring.
Purpose of the Study:
- To investigate substantial differences in acceleration and speed during team-sport locomotion between two GNSS manufacturers.
- To evaluate the impact of data filtering on GNSS-derived kinematic variables.
- To compare raw and filtered acceleration and speed data from professional rugby league athletes.
Main Methods:
- Seven professional rugby league athletes participated, wearing two 10 Hz GNSS devices (GPSports EVO and STATSports Apex) concurrently during 13 training sessions.
- Raw GNSS data were exported and processed using a standardized 1 Hz, 4th-order Butterworth filter.
- Root mean square deviation (RMSD) and linear mixed models were employed to quantify differences in speed and acceleration between manufacturers.
Main Results:
- No substantial differences were found between GNSS manufacturers for raw or filtered speed variables.
- Root mean square deviation for average acceleration decreased significantly from raw (1.77 m·s⁻²) to filtered (0.27 m·s⁻²) and twice-filtered (0.24 m·s⁻²) data.
- Raw average acceleration differed substantially between manufacturers (Apex higher than EVO), but this difference was eliminated after applying a common filter.
- Acceleration variables derived directly from proprietary software showed substantial differences.
Conclusions:
- Standardized filtering effectively minimizes differences in GNSS-derived speed and acceleration between manufacturers.
- While raw acceleration data may vary, applying a consistent filtering protocol ensures comparable results across different GNSS devices.
- The findings highlight the importance of data processing standardization for accurate and reliable athlete performance analysis in team sports.
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