- The paper presents a dual-perspective AR system that enhances stroke training by integrating on-body and detached visual cues.
- It utilizes webcam-based 3D pose estimation and IMU sensor data for real-time motion analysis and error correction using dynamic time warping.
- User studies demonstrate that the AR approach achieves superior stroke replication accuracy and boosts user confidence in performance.
Overview of avaTTAR: An Augmented Reality System for Table Tennis Stroke Training
The paper presents avaTTAR, an augmented reality (AR) system designed to augment the process of stroke training in table tennis. The system aims to improve player training by providing a comprehensive visualization of both body and paddle movements through the integration of on-body and detached visual cues. This dual-perspective visualization is meant to address several identified challenges in traditional stroke training, enabling users to enhance their skill acquisition and training outcomes.
System Design and Components
The design of avaTTAR is driven by insights from formative interviews conducted with experienced table tennis players. From these interviews, several key challenges were identified. These include the difficulty of perceiving correct stroke trajectories, the lack of effective comparison between one's stroke and that of an expert, and the need for immediate, actionable feedback during self-training sessions.
The system comprises two main components: motion capture and stroke analysis. Utilizing a webcam for 3D pose estimation and an Inertial Measurement Unit (IMU) attached to the paddle, avaTTAR can accurately reconstruct and analyze both the user's and an expert's stroke movements. The system captures these movements, facilitating real-time comparison via a dynamic time warping algorithm that aligns and contrasts player movements with those of the expert model.
Visualization Techniques
To enhance the training experience, avaTTAR employs a dual-cue visualization system. The detached view provides a third-person perspective of both the user's and expert's movements, enabling comparative analysis and correction of errors. This setup allows users to modify the view angles and pacing of the expert avatar's movements. In contrast, the on-body view superimposes virtual guidance directly onto the user's physical body, offering an immediate first-person perspective of correct motion paths.
Empirical Evaluation
A user paper validated the system's efficacy in two separate sessions. The first session focused on movement accuracy, comparing the traditional video-based method with the AR-based approach of avaTTAR. Results indicated superior performance in stroke replication when utilizing avaTTAR, with users benefitting from the intuitive cue-based guidance. The second session assessed user experience, with participants reporting heightened self-awareness of their stroke characteristics, improved confidence in stroke execution, and greater ease in following expert models.
Implications and Future Directions
avaTTAR represents a significant stride in leveraging augmented reality to facilitate motor skill learning in sports. The inclusion of both on-body and detached visual cues addresses key challenges in traditional training methodologies, enhancing both theoretical understanding and practical execution of stroke techniques. While current results demonstrate the potential of AR in stroke training, avenues for future research remain open. Potential developments include refining visual attention techniques, improving integration with dynamic game conditions, and extending the system to other sports and skill areas.
In conclusion, avaTTAR marks a promising evolution in AR-based sports training. As AR technology continues to mature, systems like avaTTAR offer valuable insights into how immersive technologies can transform skill acquisition and performance in sports disciplines. The exploration into multimodal feedback and advanced motion reconstruction could further enhance the efficacy of such systems, paving the way for more comprehensive training solutions across various domains.