Enhancing Athletic Training Through AI: A Comparative Analysis Of YOLO Versions For Image Segmentation In Velocity-Based Training

Sammanfattning

This work explores the application of Artificial Intelligence (AI) in sports, specifically comparing. You Only Look Once (YOLO) version 8 and version 9 models in the context of Velocity-Based Training and resistance training. It aims to evaluate the models’ performance in instance segmentation and their effectiveness in estimating velocity metrics. Additionally, methods for pixel to meter conversion and centroid selection on barbells are developed and discussed. The field of AI is growing vastly with great practical possibilities in the sports industry. Traditional methods of collecting and analyzing data involving sensors are often expensive and not available to many coaches and athletes. By leveraging AI techniques, this work aims to provide insights to more cost-effective solutions. An experiment was conducted where YOLOv8 and YOLOv9 models of different sizes were trained on a custom dataset. Using the resulting model weights, key Velocity-based Training (VBT) metrics were extracted from videos of squat, bench press and deadlift exercises, and compared with sensor data. To automatically track the barbell in the videos, the centroids of bounding boxes were used. Additionally, to acquire the velocity in meters per second, pixel-to-meter conversion ratios were obtained using the Circular Hough Transform. Findings indicate that the YOLOv8x model generally excels according to performance metrics, however recording high mean inference time. Additionally, the YOLOv8m model showed overestimation in mean velocity, peak velocity and range of motion highlighting potential challenges for real-time VBT applications. Otherwise, all models performed very similar to sensor data, occasionally differing in scale stemming from faulty pixel to meter conversions. In conclusion, this work underscores AI’s potential in the sports industry while identifying areas for further enhancement to ensure accuracy and reliability in applications.