AI Motion Templates for Scalable Biomechanical Sports Training
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Solution Overview
Problem
Traditional sports training methods require physical presence, limiting flexibility and scalability, and existing AI-based systems lack personalization and efficiency in analyzing biomechanical motions due to data quality and computational challenges.
Innovation Solution
A neural network-based sports training system that generates motion templates, synchronizes biomechanical motions, and stores motion features in a database for efficient analysis and feedback, enabling real-time comparison and personalized training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional in-person training sessions are used, then trainers can provide direct guidance and feedback to trainees, but the flexibility and scalability of training is limited due to physical presence requirements
Solution Approach 1:
The patent creates digital copies of trainer motions through video capture and neural network processing. These motion templates serve as virtual replicas that can be distributed to unlimited trainees simultaneously, eliminating the physical presence constraint while preserving the trainer's guidance capability.
Solution Approach 2:
The patent replaces the mechanical requirement of physical presence with an information-based system. Neural networks process video data to extract and transmit motion features digitally, substituting the physical interaction mechanism with an information processing mechanism that enables remote and scalable training.
2Productivity
If AI-based motion analysis is used, then training can be automated and scaled, but the system lacks personalization and efficiency due to data quality and computational challenges
Solution Approach 1:
The patent performs preliminary action by pre-processing video data through neural networks to extract motion templates and features before actual training analysis. This pre-extraction of motion characteristics creates a refined dataset that improves subsequent analysis accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent extracts essential motion features from raw video data using neural networks. By separating and storing key motion characteristics in structured templates, the system reduces computational complexity for real-time analysis while preserving the precision needed for accurate motion evaluation.
3Measurement precision
If comprehensive motion data is collected for accurate analysis, then training precision improves, but computational burden increases
Solution Approach 1:
The patent extracts only the essential motion features needed for training analysis rather than processing all raw video data continuously. By identifying and isolating key motion parameters, the system maintains high analysis accuracy while significantly reducing computational resource requirements.
Solution Approach 2:
The patent performs preliminary extraction of motion templates and features from video data before actual training sessions. This pre-processing creates optimized data structures that can be reused across multiple trainees, eliminating redundant computational work while preserving measurement precision.
Data Source
AI summary
This disclosure pertains to a sports training system that utilizes deep learning and computer vision technologies to analyze biomechanical sport motions. The system is equipped with AI-powered functionalities to enhance training, including those related to creating and managing motion templates that integrate video sequences with extracted motion features. These templates serve as benchmarks, accessible to trainees for improving their technique and performance. The system also can employ an advanced motion synchronization process, which combines dynamic time warping and computer vision technologies to align trainee videos with reference motions captured in motion templates. Additionally, a motion feature database can be updated with features extracted from both trainee and trainer videos, storing visual information in a structured format to facilitate various analytic functions. The system supports analysis of a wide range of sports motions.