ML Motion Analysis for Tennis Skill Training
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Solution Overview
Problem
Low-income teenagers face barriers in accessing tennis training due to the high cost of coaching and limited availability of affordable technology for analyzing and improving their tennis skills, which are essential for developing proficiency in the sport.
Innovation Solution
A machine-learning based motion analysis and training system that uses widely available electronic devices like smartphones and wearable IMUs to collect and analyze kinematic data, providing feedback and guidance for improving tennis skills, thereby reducing the need for expensive coaching and specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional expert coaching and specialized analysis systems are used, then tennis skill development quality is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent creates a simplified copy of professional coaching systems using machine learning models that replicate expert analysis capabilities. The ML model learns from professional coaching data and reproduces skill evaluation functions, making expert-level analysis accessible through simple smartphone-based IMU sensors without requiring actual expert coaches or complex specialized equipment.
Solution Approach 2:
The patent replaces the mechanical system of human expert coaches with an automated machine learning-based analysis system. Instead of relying on human experts to observe and evaluate tennis movements, the system uses ML algorithms processed through smartphone sensors to automatically analyze and provide feedback on tennis skills, eliminating the need for expensive human coaching resources.
2Reliability
If professional coaching services are accessed, then tennis skill improvement is enhanced, but financial cost increases
Solution Approach 1:
The patent enables tennis players to conduct self-training and self-evaluation using the ML-based system. Players can independently analyze their own movements, receive automated feedback, and improve their skills without requiring external coaching resources. The system provides reliable skill development guidance that was previously only available through expensive professional coaches, making effective training accessible to players regardless of their financial situation.
3Loss of information
If comprehensive motion analysis is performed, then feedback quality is improved, but data processing complexity and time increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models with extensive motion data before actual tennis analysis. The ML models are trained in advance on comprehensive motion capture data to learn the relationships between various movement parameters and skill quality. This preliminary training enables the system to provide rapid, accurate feedback during actual tennis practice without requiring time-consuming real-time complex calculations, thus retaining motion detail information while minimizing analysis time during use.
Data Source
AI summary
A machine-learning (ML) based motion analysis and training method and systems are provided. The method includes acquiring data of measurements of kinematics of a user performing a task with his/her hands or arms; analyzing the data of measurements based on a machine-learning (ML) model to evaluate patterns of hand/arm movements of the user; and providing feedback and/or advice based on results of the analysis for improvement of skills of the hand/arm motions of the user. The analyzing the data of measurements includes data preprocessing for preparing raw data for analysis, feature engineering for creating relevant features from the raw data for training the ML model, model training for training the ML model based on the preprocessed data, model evaluating for assessing performance of the trained model based on predetermined evaluation metrics, hyperparameter tuning for fine-tuning the hyperparameters of the ML model to improve its performance, model validating, and model testing.