Machine Learning Model for Sports Motion Feedback
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Solution Overview
Problem
There is a need for an efficient and cost-effective method to remotely teach and learn specific motions, such as a golf swing, as traditional methods require extensive time, capital, and effort, and often lack direct expert guidance.
Innovation Solution
An electronic device and operating method utilizing a machine learning model based on data from expert athletes, which receives input on user motion and provides feedback through a 3D graphical interface, allowing for remote evaluation and guidance on improving specific sports motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional face-to-face expert guidance is used for teaching specific motions, then learning accuracy and quality are improved, but learning time, capital, and effort increase significantly
Solution Approach 1:
The patent creates a digital copy of the expert's motion through machine learning model training. The system captures expert motion data (skeleton information, angular velocity, evaluation criteria) and generates a virtual coach that can be replicated and distributed remotely, eliminating the need for physical presence while maintaining teaching quality
Solution Approach 2:
The machine learning model serves as an intermediary between the expert and the user. It processes user motion data, compares it against learned expert patterns, and provides automated feedback, replacing direct human-to-human interaction with an automated mediation system that reduces time and resource requirements
2Measurement precision
If traditional face-to-face expert guidance is used for teaching specific motions, then learning accuracy and quality are improved, but capital and effort required for practice increase
Solution Approach 1:
The system enables self-service learning where users independently practice and receive automated feedback without requiring expert intervention. The machine learning model autonomously evaluates user performance and provides guidance, eliminating the need for expensive expert time while maintaining learning effectiveness
Solution Approach 2:
By creating a digital replica of expert knowledge in the machine learning model, the system eliminates the need for repeated expert involvement. Once the model is trained, it can serve unlimited users simultaneously without additional capital investment in expert time or travel
3Productivity
If remote learning method is used to reduce time and cost, then learning efficiency is improved, but feedback accuracy and guidance quality deteriorate
Solution Approach 1:
The patent replaces the mechanical system of human expert evaluation with an automated machine learning-based evaluation system. It uses computer vision to capture user motion, extracts skeleton and angular velocity data, and applies machine learning models to provide automated feedback, maintaining accuracy while enabling remote operation
Solution Approach 2:
The system implements automated feedback loops where user motion is continuously captured, analyzed against the machine learning model, and evaluated in real-time. This provides immediate corrective guidance without waiting for expert review, maintaining feedback quality while improving learning efficiency
Data Source
AI summary
An operating method of an electronic device includes receiving a program including a machine learning model generated by performing machine learning using, as training data, information on a plurality of skeletons associated with a specific motion of an expert and/or a professional athlete associated with a specific sport, information on a plurality of angular velocities associated with the specific motion, and/or a plurality of pieces of evaluated information associated with the specific motion, which are accumulated in a server, from the server, and executing the program and receiving information on second angular velocity for the specific motion of a user of the electronic device from a swing practice device based on execution of the program.