AI Table Tennis Coach
This dynamic Israeli company wants to revolutionize how table tennis players and coaches analyze their games' videos with the help of AI. Our clients believe that anyone, regardless of their level, can improve their table tennis training, game and technique by video analysis. Clubs, leagues and broadcasters can also use this service to automatically create highlight clips of table tennis matches.
The global sports analytics market is poised for remarkable growth, with a projected compound annual growth rate of 27.3% from 2021 to 2030. Starting at an estimated 2.45 billion U.S. dollars in 2021, the market is anticipated to grow to nearly 22 billion U.S. dollars by 2030, according to Statista.
The landscape of sports technology is evolving, and AI is revolutionizing performance analytics and making significant strides in virtual training tools and AI training solutions. These tools have inundated the market, catering to players of all proficiency levels, offering unprecedented insights and personalized coaching experiences.
Table tennis stands out among sports for the seamless integration of AI due to its inherently data-driven nature and the high speed of the game. In this sport, data plays a pivotal role at every level and the demand for accurate and reliable information is escalating with its technical intricacies.
Our client has already developed a functional AI trainer solution deployed on a server. In alignment with the ever-evolving technological landscape, they are keen on enhancing the adaptability of their AI table tennis coach solution to meet the demands of contemporary mobile devices, beginning with iPhones. Our task was to bring cutting-edge AI coaching directly to users’ palms, empowering them to elevate their table tennis skills through an intuitive and accessible mobile platform.
- Duration: October 2023 - Ongoing
- Location: Israel
- Industry: Sport and Wellness
- Project discovery, MVP development
Optimized Processing Execution:
– Due to the iOS background task interface limiting processing duration to 2 minutes, aligning processing activities with active AI trainer app usage is crucial.
– This ensures efficient utilization of the available processing time, emphasizing the need for strategic scheduling and prioritization of tasks.
Cross-Platform Runtime Optimization:
Recognizing the runtime limitations imposed by cross-platform support, the project should focus on compatibility with ONNX and TFLite runtimes. Given the superior support for GPU operations, CoreML delegates, and the native exporting pipeline in TensorFlow, TFLite emerges as the preferred choice. Aligning these considerations will enhance the overall efficiency and performance of the AI table tennis coaching solution.
Model Adaptation for iOS GPU Acceleration:
Acknowledging the absence of support for bidirectional LSTM on iOS with GPU acceleration, the table tennis AI coach project needs to explore alternative approaches for model execution. This may involve restructuring the model or utilizing other suitable techniques to achieve optimal performance on iOS devices, aligning with the platform’s GPU capabilities and constraints.
Automated Game Highlights: Automatically identifies and generates highlights from table tennis game videos.
Technique Analysis: Provides detailed analysis of players' techniques, offering insights for improvement.
Multi-Angle Video Support: Analyzes table tennis games from various angles, including perspectives from players, coaches, and umpires.
Idle Moment Elimination: Identifies and removes idle moments in the game videos to focus on key action sequences.
Club and League Integration: Supports clubs and leagues by automating the creation of highlight clips for matches.
Coach Collaboration Feature: Facilitates collaboration between players and coaches through shared analysis and feedback.
Compatibility with Popular Video Formats: Supports a variety of video formats commonly used in smartphones and cameras.
AI-Powered Trend Identification: Utilizes AI algorithms to identify trends in gameplay strategies and techniques.
Documentation and Insights Report: Generates comprehensive documentation and insights reports to aid in strategic planning and improvement.
Our client already has a working table tennis AI trainer model functioning on a server. They wanted to improve the compatibility of their AI tennis coach solution with the latest mobile devices, starting with iPhones.
Through MVP application development and iterative experimentation, we optimized the initial AI coach model and provided comprehensive documentation addressing the client’s inquiries:
1. Code Analysis and Overview: Provided an in-depth examination and summary of the code.
2. CPU Model on Device and iOS App Development: Discussed the CPU model running on the device and its implications for iOS app development.
3. CPU+GPU Model on Device and iOS Camera App Development: Explored the integration of CPU+GPU models on the device, emphasizing its relevance to iOS camera app development.
4. Memory Availability Check: Ensured that the device has sufficient memory for optimal AI coach model performance.
5. Accuracy Assessment Post Model Export: Examined if the model’s accuracy remains consistent after exporting it to the device.
6. Inference Time on iOS: Evaluated the time taken to infer an original model on the iOS platform.
7. Exporting Results Text Log to Computer and Accuracy Comparison: Discussed exporting results’ text logs to a computer and comparing accuracy.
8. User-Independent Video Processing (In Progress): Updated on the ongoing efforts to enable video processing without user participation.
9. Optimizing Inference without High Power Consumption (Next in Line): Highlighted upcoming steps to ensure inference optimizations without excessive power consumption.
10. Documentation of Results: Emphasized the importance of thorough documentation of the obtained results.
Our technology stack
- TensorFlow Lite
- Coco API
– Our table tennis video analysis software significantly reduces the time spent on video analysis.
– Users can focus more on improving techniques and identifying patterns rather than investing excessive time in manual video editing.
Precision and Accuracy:
– Unmatched accuracy in identifying and analyzing gameplay, including the ability to detect unforced errors.
– Users can trust the table tennis video analysis results to make informed decisions on refining their playing strategies.
Effortless Dead Time Removal:
– Automated elimination of non-playing time from videos.
– Saves users from the tedious task of manual editing, providing a streamlined and efficient table tennis video analysis experience.
Versatility in Analysis Angles:
– Exclusive capability to analyze games from various angles, including the coach’s perspective, the umpire’s side, and more.
– Ensures AI trainer app users can choose the most practical angle for recording matches, overcoming limitations imposed by space constraints in clubs and competitions.
Compatibility and Accessibility:
– Our AI-powered tennis trainer software is compatible with any angle, capturing the table and both players.
– Users aren’t restricted by camera placement challenges, providing flexibility in recording matches in diverse settings.
Tailored for Clubs and Competitions:
– Recognizes and addresses the common limitations faced by players in clubs and competitions regarding camera placement.
– Ensures that our table tennis AI coach solution aligns with the practical realities of recording matches in typical club and competition environments.
Holistic Game Insight:
– Enables users to not only improve specific techniques but also identify trends and patterns in their play.
– Offers a comprehensive understanding of one’s gameplay, facilitating strategic improvements for overall performance enhancement.
– Our algorithm is unique and unavailable in other table tennis AI trainer solutions.
– Users gain access to cutting-edge technology, ensuring they have a competitive edge in their tennis video analysis compared to existing alternatives.