Adaptive Fitness Planning Using Sensor Feedback and Force Metrics
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Solution Overview
Problem
Existing fitness programs fail to accurately predict user results and efficiently improve physical capabilities due to lack of integration with user-specific data from sensors and inability to dynamically adjust exercise schedules based on real-time performance.
Innovation Solution
A system that integrates with external sensors, such as shoe sensors, to generate personalized fitness programs tailored to individual goals and capabilities, dynamically adjusting schedules based on real-time performance data to ensure optimal exercise timing and progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fitness programs are based on predetermined schedules for average users, then the programs are simple to implement, but they cannot accurately predict or optimize results for individual users with unique physical capabilities
Solution Approach 1:
The system changes parameters by collecting multiple physical capability metrics (force production, power output, speed, agility) and using them to dynamically adjust exercise prescriptions. Instead of fixed schedules, the program adapts exercise selection, intensity, and frequency based on measured parameters and predicted physiological responses, achieving accurate individualized predictions.
Solution Approach 2:
The system implements feedback loops where sensor data from exercise performance is continuously fed back into the prediction model. The model uses this feedback to refine its predictions of physiological responses and adjust future exercise recommendations, enabling progressively more accurate prediction of individual user results.
2Productivity
If fitness programs do not integrate sensor data, then they are easier to implement, but they cannot efficiently obtain or utilize user physical capability information
Solution Approach 1:
The system enables self-service by automatically collecting physical capability data through sensors during exercise performance. The sensors autonomously measure force production, power output, speed, and agility without requiring manual input from users. The system processes this data automatically to generate personalized fitness recommendations, eliminating the need for users to manually provide capability information.
Solution Approach 2:
The system uses sensors as intermediaries between the user's physical performance and the fitness program generation. These sensors (such as force plates, accelerometers, or GPS devices) automatically capture physical capability data and transmit it to the prediction model, which then generates appropriate exercise prescriptions without requiring direct user intervention in the data collection process.
3Adaptability or versatility
If fitness programs cannot dynamically adjust to real-time performance, then they are simpler to manage, but they cannot optimize exercise timing and progression for individual users
Solution Approach 1:
The system applies dynamics by making the exercise schedule adjustable and adaptive rather than fixed. The program dynamically modifies exercise selection, intensity, frequency, and timing based on real-time sensor data and predicted physiological responses. This dynamic adjustment allows the system to optimize fitness outcomes for each user while automatically managing the complexity of program implementation.
Data Source
AI summary
Systems, methods, devices, and computer programs for generating personalized fitness programs are disclosed. One aspect comprises a method including inducing a release of a protein expression into the body, measuring a force generated by the body utilizing over ninety percent of skeletal muscle to generate the force, determining a force metric based on the force generated in relation to a parameter of the body, identifying a target force metric based on at least one user identified target metric, measuring at least one physical metric of the body, generating personalized training regimen based on the determined force metric and the at least one identified target metric, the developed training regimen configured to maximize conversion of type 1 muscle cells to type 2b muscle cells, and wherein the personalized training regimen is designed to be implemented by the body while the protein expression is present in the bloodstream of the body.