Studying the Biomechanics of a Wheelchair Basketball Free Throw using Pose Estimation

Abstract

Wheelchair basketball is a popular Paralympic sport where athletes with varying disabilities compete under a point-based classification system. Lower-class athletes (1.0–2.5), with higher levels of disability, often struggle to engage their trunk and core muscles, while higher-class athletes (3.0–4.5) have greater functional ability and utilize their trunk extensively. Coaches must consider these functional disparities when formulating strategies and designing individualized training regimens.

Consistent free-throw shooting is critical in wheelchair basketball, as it offers an uncontested scoring opportunity. Higher-class athletes, who incorporate trunk motion, rely less on their arms for force generation, resulting in distinct shooting mechanics. Given the biomechanical variability arising from these physical differences, understanding individual shooting techniques is vital for optimizing performance.

Motion capture technologies are widely employed to analyze and improve athletic movements. However, traditional systems, such as wearable sensors and marker-based motion tracking, are often costly, time-intensive, and restrictive to mobility. Markerless motion capture systems address these limitations using computer vision techniques like pose estimation. Convolutional neural networks (CNNs) trained on large human image datasets can accurately detect joints and limbs, enabling real-time analysis. Commercial systems typically require multiple cameras, but deploying pose estimation CNNs on mobile devices allows motion analysis using only a built-in camera, enhancing portability and accessibility for sports training and biomechanical research.

This research focuses on designing and deploying pose estimation models within a mobile application to analyze the shooting arm's motion during a basketball free throw, with specific considerations for wheelchair basketball players. The pose estimation models, trained on a COCO-WholeBodyTM dataset to detect fingertip positions, were deployed on an iPhone and tested for accuracy and computational performance, particularly real-time motion analysis. The derived joint positions are used to calculate kinematic and dynamic metrics, including joint angles and torques.

The system's joint angle calculations were compared against the Vicon motion capture system. While upper arm and elbow angle errors had a root mean squared error (RMSE) within an acceptable range (less than 20◦), wrist angle errors exceeded 65◦ due to limitations in pose estimation accuracy and the iPhone camera's frame rate. To demonstrate the system's utility, two shooting studies were conducted: (1) a comparison of biomechanics between one-motion and two-motion shooting techniques and (2) a biomechanical analysis of the shooting arm contrasting a national-level class 1 wheelchair basketball athlete with class 4.5 able-bodied participants shooting from a basketball wheelchair.