Sensor-Driven Fitness Planning for Adaptive Force Training
Find Innovative SolutionsGenerate Solutions
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
1Adaptability or versatility
If personalized fitness programs are highly complex and involve various facets of an individual's life, then the program can provide comprehensive fitness solutions, but the program becomes difficult to implement and track
Solution Approach 1:
The patent segments the complex fitness program into distinct modules: initial capability assessment, target capability setting, program generation, real-time tracking, and predictive analytics. Each module handles a specific aspect of the fitness journey, making the overall system manageable and implementable while maintaining comprehensive personalization.
Solution Approach 2:
The patent introduces a computational system acting as an intermediary between the user and the complex fitness program. This system automatically processes sensor data, generates personalized programs, predicts outcomes, and adjusts regimens without requiring direct user intervention in the complex underlying logic, thus simplifying implementation while maintaining versatility.
2Ease of operation
If fitness programs provide predetermined schedules based on average sampling of users, then the program is easy to implement, but the program cannot accurately predict individual user results
Solution Approach 1:
The patent performs preliminary actions by conducting an initial assessment of the user's current physical capabilities before generating the fitness program. This baseline measurement, combined with the user's target goals, enables the system to create a personalized prediction model that forecasts expected results, thereby maintaining ease of operation while significantly improving prediction accuracy compared to average-based approaches.
Solution Approach 2:
The patent implements continuous feedback loops where sensor data from the user's exercise performance is collected, analyzed, and used to update the prediction model. This real-time feedback allows the system to refine its accuracy for individual users while maintaining the simplicity of predetermined schedule structures, resolving the contradiction between ease of operation and prediction precision.
3Measurement precision
If fitness programs use sensors to identify and measure user's physical capabilities, then the program can generate accurate predictions, but the system requires integration of multiple components increasing complexity
Solution Approach 1:
The patent employs a multi-functional computational system that handles diverse tasks: collecting data from multiple sensor types, processing initial capability assessments, generating personalized fitness programs, tracking real-time performance, and producing predictive analytics. By consolidating these functions into a single integrated platform, the system achieves high measurement precision without proportionally increasing overall system complexity.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically calibrates sensors, validates data quality, and adjusts measurement parameters based on user feedback and performance patterns. This self-service capability reduces the need for manual configuration and integration complexity while maintaining high measurement accuracy for physical capabilities.
4Productivity
If fitness programs dynamically adjust exercise schedules based on real-time performance data, then the program optimizes exercise timing and progression, but the program requires continuous monitoring and adjustment increasing operational complexity
Solution Approach 1:
The patent implements dynamic adjustment of exercise schedules by continuously monitoring real-time performance data from sensors and automatically modifying the fitness program parameters. The system adjusts exercise intensity, duration, and timing based on measured performance metrics, enabling optimized productivity while managing complexity through automated decision-making algorithms that replace manual program management.
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.