In 2021, the global sports biomechanics market was estimated to be worth around $120 million to $150 million. This market size will continue growing with ongoing advancements in motion capture, sensor technology, and data analysis tools. Wearable devices, such as smart clothing and sensors, are becoming more popular for real-time biomechanical analysis.
Investment in research and development in the field of sports biomechanics is surging, and an essential component of this research is data annotation. This process involves labeling and marking specific events or characteristics in motion capture data, video recordings, or other sources. However, data annotation in sports biomechanics is not without its challenges, and this blog explores some of the most significant hurdles researchers and analysts face when annotating sports biomechanics data.
Subject Variability
One of the primary challenges in sports biomechanics data annotation is the wide variability among athletes. Athletes come in different shapes, sizes, and skill levels. Their unique biomechanical profiles mean that what is considered a standard or correct movement for one athlete may not be the same for another. This subject variability complicates the development of standardized annotation systems that can be applied universally.
Data Quality
The accuracy and quality of the data used for annotation are paramount. Sports biomechanics relies on data from various sources, including motion capture systems, force plates, and video recordings. Errors, noise, or inconsistencies in these data can significantly affect the accuracy of the annotations. It is essential to calibrate and validate data acquisition systems to minimize inaccuracies and ensure the reliability of annotations.
Inter- and Intra-Rater Reliability
Reliability is a crucial consideration in data annotation. Inter-rater reliability refers to the consistency of annotations made by different individuals or analysts, and intra-rater reliability concerns the annotation consistency made by the same person over time. Lack of consistency can introduce errors in the analysis and affect the validity of the results.
Annotating Complex Movements
Sports often involve complex and multifaceted movements that can be difficult to annotate accurately. Events like cutting, jumping, and throwing require analysts to identify multiple key points, angles, and timings. Annotating complex movements may require expertise and experience in sports biomechanics to ensure subtle nuances.
iMerit has 10+ years of experience in multi-sensor annotation for the camera, LiDAR, radar, and audio data to enhance scene perception, localization, mapping, and trajectory optimization. Here is a sneak peek at how iMerit’s human-in-the-loop model overcomes all the mentioned data challenges by combining the right technology, talent, and technique.
iMerit for Sports Biomechanics Data Annotation
By providing game-changing insights for injury prevention, rehabilitation, and performance enhancement, iMerit’s team of annotators, sports medicine experts, therapists, and computer vision specialists supports AI projects in the field of Sports and Biomechanics.
- Expert-in-the-loop data processes and automation tools to generate high-volume training data for human movement analysis, sports performance analysis, and biomechanics applications
- US board-certified physicians for facilitating regulatory approval of clinical solutions
- Experience annotating data from motion capture and biomechanical sensors to analyze player movements to assess speed, agility, strength, endurance, and skill proficiency
Conclusion
Data annotation is a fundamental step in understanding and optimizing athletic performance and injury prevention. However, it comes with several challenges, including subject variability, data quality, reliability, temporal synchronization, subject fatigue, and the complexity of annotating intricate movements. Overcoming these challenges is essential for producing accurate and meaningful results in sports biomechanics research. Researchers and analysts must continuously refine their annotation processes and invest in rigorous training and quality control measures to address these hurdles successfully.
