Tennis Stroke Recognition System Using Gesture Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current training methods for tennis players lack a reliable and automated system to improve tennis stroke recognition, which is crucial for skill development, as existing computer-based visual skills training programs are not tennis-specific and do not provide real-time feedback on stroke recognition and anticipation.
Innovation Solution
A system and method that uses a computer to display videos of tennis strokes, allowing users to select and predict the type of stroke and ball location through gestures or voice, providing immediate feedback on accuracy and assigning points for correct identification, with the system determining the actual stroke type and ball location for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to recognize tennis strokes, then training efficiency is improved, but reliability of stroke recognition is insufficient
Solution Approach 1:
The system segments the tennis stroke recognition task into multiple independent analysis components: racquet trajectory analysis, ball trajectory analysis, contact point detection, and stroke type classification. Each component processes specific aspects of the stroke independently, then combines results to achieve reliable overall recognition while maintaining high processing efficiency
Solution Approach 2:
The system introduces an intermediary computer vision processing layer that acts as a mediator between the video input and the recognition decision. This intermediary layer performs automated frame-by-frame analysis, detects key moments (racquet-ball contact), and extracts features that reliably indicate stroke type, thereby improving both accuracy and efficiency
2Measurement precision
If tennis-specific visual skills training is implemented, then stroke recognition ability is improved, but device complexity increases
Solution Approach 1:
The system uses a universal computer-based platform that can perform multiple functions: displaying training videos, capturing user predictions, detecting gestures, providing feedback, and tracking progress. This multi-functional approach achieves tennis-specific training without requiring complex specialized hardware, thereby improving measurement precision while controlling device complexity
Solution Approach 2:
The system employs automated computer vision algorithms that perform stroke recognition and validation without human intervention. The system automatically compares user predictions against actual stroke outcomes, provides immediate feedback, and tracks learning progress, eliminating the need for complex manual scoring and evaluation systems
3Measurement precision
If real-time feedback is provided on stroke recognition, then learning effectiveness is improved, but loss of time in processing increases
Solution Approach 1:
The system performs preliminary processing by pre-analyzing video frames to identify and mark key moments such as racquet-ball contact points before the user makes their prediction. This preliminary action prepares the data structure and extracts relevant features in advance, enabling rapid comparison with user predictions and providing immediate feedback without significant processing delays
Solution Approach 2:
The system implements an immediate feedback loop where user predictions are automatically compared against the computer-vision-determined actual stroke type and ball location. The feedback is provided in real-time after each prediction, allowing users to immediately learn from their errors and improve their stroke recognition ability without time loss
Data Source
AI summary
Certain embodiments relate to systems and methods for improving tennis stroke recognition that includes: outputting on an output device a video or animation of an opponent player executing a tennis stroke, a layout of the near court, and one or more tennis stroke indicators corresponding to different tennis strokes; detecting a first user gesture or voice utterance adjacent to the output device to select one of the tennis stroke indicators a user believes is executed by the opponent player; detecting a second user gesture adjacent to the output device identifying a location in the layout of the near court the user expects the ball to hit; and updating the output device to display an actual type of tennis stroke executed by the opponent player and an actual location in the layout of the near court the ball hits as a result of the actual tennis stroke executed by the opponent.