AI Workout Guide Apparatus Personalizing Target Weight
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Solution Overview
Problem
Users face difficulties in determining the appropriate weight for fitness equipment based on their individual characteristics, workout capability, and purpose, leading to ineffective workouts.
Innovation Solution
An AI workout guide apparatus that estimates and adjusts the target weight of fitness equipment using user data such as gender, age, weight, height, BMI, body fat percentage, and individual objectification indexes like athletic performance, to provide personalized and optimal workout settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed initial target weight is provided to all users with the same demographic characteristics, then the system is simple to operate, but it fails to account for individual athletic performance differences
Solution Approach 1:
The system performs preliminary actions by collecting user data (gender, age, weight, height, BMI, body fat percentage) and estimating muscular strength before providing target weight recommendations. This preliminary assessment enables personalized workout guidance without requiring complex real-time analysis during exercise sessions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring individual objectification indexes (weight used, number of reps, number of sets, workout trajectory, moving velocity, regularity of reps) and updating the PMW individual based on performance data. This feedback loop allows the system to adapt target weights dynamically while maintaining operational simplicity for users.
2Measurement precision
If target weight is based only on demographic data, then the calculation is simple, but it does not reflect individual workout capability or purpose
Solution Approach 1:
The system segments the target weight determination process into distinct components: PMW estimation based on demographic data and muscular strength estimation, and PMW individual adjusted by individual objectification indexes. This segmentation allows the system to progressively refine accuracy without overwhelming computational complexity.
Solution Approach 2:
The system changes parameters by transitioning from static demographic parameters to dynamic performance parameters. By incorporating individual objectification indexes that capture workout-specific metrics (reps, sets, trajectory, velocity), the system achieves precise target weight estimation that reflects actual workout capability and purpose.
3Productivity
If the system continuously updates target weights based on performance data, then workout optimization is achieved, but data collection and processing requirements increase
Solution Approach 1:
The fitness equipment is designed with multi-functionality, serving both as exercise equipment and data collection device. Sensors and detectors integrated into the equipment automatically capture individual objectification indexes during normal workout sessions, eliminating the need for separate data collection systems and reducing overall system complexity.
Solution Approach 2:
The system implements self-service by automatically collecting performance data, calculating PMW individual, and updating target weights without requiring manual intervention. The equipment monitors its own usage parameters and autonomously adjusts recommendations, improving workout effectiveness while minimizing data processing burden on users.
Data Source
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AI summary
Provided is an artificial intelligence (Al) workout guide apparatus including a personal maximum weight (PMW) estimator configured to estimate PMWestimation of fitness equipment to be used by a user, based on an estimated muscular strength value calculated based on user data, and a PMW guider configured to provide PMWindividual for the fitness equipment by complementing the PMWestimation with an individual objectification index of the user