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A comparison between manual and automated event detection for shuffle, deceleration and run cut tasks using motion
Alex M Loewen1, Jan Karel Petric2, Hannah L Olander1
1Scottish Rite for Children, Frisco, TX, USA.
Clinical Biomechanics (Bristol, Avon)
|August 6, 2025
Summary
Automated event detection improves the reliability of analyzing movement data for injury prevention. This method reduces variability compared to manual analysis, enhancing accuracy in clinical and research settings for athletes.
Area of Science:
- Biomechanics
- Sports Medicine
- Data Analysis
Background:
- Adolescent sports participation is rising, leading to more sports-related injuries.
- Injury prevention requires accurate analysis of movement mechanics during tasks like shuffling and cutting.
- Automated event detection algorithms are emerging to enhance 3D motion capture data processing.
Purpose of the Study:
- To compare the reliability of manual versus automated event detection in analyzing movement tasks.
- To assess differences in event timings and task performance between detection methods.
- To evaluate the utility of automated detection for injury risk assessment in athletes.
Main Methods:
- Healthy controls and adolescents post-anterior cruciate ligament reconstruction performed shuffle, deceleration, and run-cut tasks.
- Movement data was processed using both manual identification by raters and custom MATLAB algorithms.
- Intra- and inter-rater reliability, event timings, and task performance were compared.
Main Results:
- Significant differences in event timings were observed between manual and automated detection (4.7-13.5 frame differences).
- Foot contact timepoints showed similar identification between manual and automated methods.
- Automated detection demonstrated greater reliability in identifying key movement events.
Conclusions:
- Automated event detection offers a more reliable method for analyzing participant movement timepoints.
- This approach minimizes user variability, providing consistent event identification for injury risk assessments.
- Automated detection is valuable for both clinical and research applications in sports injury prevention.
