AI Motion Recognition for Home Exercise Reward Systems
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Solution Overview
Problem
Conventional home training services lack precision in motion recognition, require additional equipment, and have insufficient motivation for consistent exercises, limiting their effectiveness compared to offline training.
Innovation Solution
A method and apparatus using artificial intelligence to analyze a user's exercise posture and provide real-time rewards, including competition features and personalized content based on exercise achievement, to enhance motivation and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion recognition technology is used for home training, then the service can be provided without additional equipment, but the precision in measuring user motion is limited
Solution Approach 1:
The patent replaces conventional mechanical motion capture systems with an AI-based visual recognition system. The server uses machine learning models to analyze video frames from a standard camera, extracting skeletal information and motion data without requiring specialized mechanical sensors or equipment at the user end. This substitution achieves high measurement precision using only a regular camera and computational algorithms.
Solution Approach 2:
The patent introduces an intermediary AI processing server that acts as a mediator between the user's home training environment and the motion analysis system. The server receives video data, performs complex motion recognition and precision measurement through AI models, and returns analysis results to the user terminal. This intermediary handles the computational complexity centrally, allowing simple user-side equipment to achieve precise motion measurement.
2Ease of operation
If home training services are provided without additional equipment, then ease of operation is improved, but motivation for consistent exercises is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the AI system continuously monitors user motion, provides real-time posture correction guidance, and tracks exercise progress. The system compares actual user movements against standard exercise patterns, offers immediate corrective feedback, and maintains exercise logs. This continuous feedback loop enhances user motivation and ensures consistent exercise performance without requiring additional equipment or in-person coaching.
3Measurement precision
If AI motion recognition precision is enhanced, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the motion recognition system into distinct functional modules: video capture module, AI processing module for skeletal extraction, motion analysis module, and feedback generation module. Each module performs a specific function in the motion recognition pipeline. This segmentation allows the complex AI processing to be distributed and managed separately, reducing the perceived complexity at any single point while maintaining high overall precision.
Data Source
AI summary
The present disclosure provides a method for providing rewards based on exercise amount measurement performed by a reward providing apparatus. The method according to an embodiment may comprise: receiving a video image of a user's body inputted through an image input part of a user terminal; recognizing a motion corresponding to a pre-set exercise in the video image of the user's body using a pre-learned artificial intelligence model for motion recognition; measuring an exercise amount based on the recognized motion; providing a customized content to the user through a first area of an image output part based on the measured exercise amount and at least one piece of the user's information; and providing a reward to the user based on a watch result of the provided content.