Opportunity
Learning and training in sports like tennis traditionally involves self-practice, group lessons, or private coaching. While private coaching is effective for rapid improvement, it is expensive and often inaccessible due to scheduling conflicts or lack of court access. Online tutorials and videos provide guidance but lack personalized feedback—a player cannot know if their serve posture is correct just by watching a video. Existing motion analysis systems often require expensive motion capture equipment or wearable sensors, making them impractical for casual players or home use. There is a need for an affordable, accessible, AI-powered training system that uses only a standard camera to analyze a player's posture and movements, provide real-time corrective feedback, and track progress over time.
Technology
This patent presents a video-based sport training system (e.g., "TennisSpark") that uses a standard camera module (e.g., on a tablet or laptop) to capture images or video of a user performing tennis movements. A posture analyzing module processes these images using pose estimation to detect whether the user has correctly performed a predetermined move (serving, forehand, double backhand, single backhand, smashing, backhand slicing).
The system identifies valid body gestures composed of the user's stance and the positions of arms/hands relative to the torso. For each skill, the system extracts key postures (3-4 frames from the movement) and compares them against stored valid templates. The user receives real-time feedback via the feedback module: success messages (text + voice) or failure messages describing what went wrong (e.g., "follow-through incorrect").
The system operates in multiple modes: Demo Mode (video tutorials), Coach Mode (real-time posture evaluation with recording/playback), Repetitive Training Mode (counts correct repetitions out of a target), Practice Mode (virtual ball machine shoots balls at random angles), and Game Mode (multiplayer online matches). Users can control the system via hand gestures (pointing up/down/left/right, OK sign, victory sign) or body gestures (T-pose, cross-arm, hands-on-chest) to navigate menus, pause/resume, or record.
Advantages
- No Wearable Sensors Required: Uses only a standard camera (tablet/laptop webcam) for pose estimation—no expensive motion capture suits or IMUs.
- Real-Time Corrective Feedback: Provides immediate text and voice guidance on what the user did wrong (e.g., "raise left arm before smash").
- Multiple Training Modes: Demo, coach, repetitive training, practice (virtual ball machine), and multiplayer game mode—covers learning to competition.
- Gesture-Based Hands-Free Control: Navigate menus and control playback using body gestures (T-pose, cross-arm) or hand gestures—no touching the screen.
- Visual & Audio Feedback: Combines on-screen text, voice instructions, and visual overlays (avatar + real-time video) for accessible coaching.
- Progress Tracking: Counts successful repetitions in training mode; displays summary scores (e.g., "70/100 serves successful").
- Accessible & Affordable: Runs on common devices (PCs, tablets, phones), enabling home training without hiring expensive coaches.
Applications
- Tennis Training at Home: Players can practice serves, forehands, and backhands with real-time posture correction without a court or coach.
- School Physical Education: Affordable large-scale tennis instruction for students using tablets or laptops.
- Sports Rehabilitation: Patients recovering from arm or shoulder injuries can practice controlled movements with posture verification.
- Multiplayer Online Gaming: Remote tennis matches between friends or strangers using webcams for motion input.
- Virtual Ball Machine Practice: Solo practice against an AI-controlled ball launcher that varies shot angles.
