Personalized Strength Curve Algorithm for Weight Training

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Solution Overview

Problem

Existing strength training methods fail to accurately determine and adapt to individual muscle fatigue and resistance, leading to inefficient workouts as users' strength levels change over time.

Innovation Solution

A system and method using algorithms to generate a personalized strength curve, known as the Lynch Baseline Strength Curve, which adjusts based on user data to recommend ideal target weights and repetitions, incorporating muscle fatigue testing and data analysis through a mobile app and server connection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If known strength curves and workout plans are used, then exercise sets and repetitions can be suggested, but they fail to accurately indicate or suggest the ideal target weight or resistance for a wide variety of users

Engineering Contradiction:

Improveadaptability to different usersVSAvoidaccuracy of target weight recommendation

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary action by having users complete a baseline strength test before generating personalized strength curves. This preliminary data collection enables accurate target weight recommendations by establishing individual strength baselines against which future performance is measured and compared.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring user performance across multiple exercises and using this data to dynamically adjust and refine personalized strength curves. This feedback loop ensures both high adaptability to individual users and precise target weight recommendations through iterative optimization.

Inventive Principle:

Principle #23Feedback

2Stability of the object's composition

If fixed workout plans are used, then exercise structure is provided, but they fail to adapt to changes in user strength levels over time

Engineering Contradiction:

Improveconsistency of workout structureVSAvoidadaptation to strength changes

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system applies dynamics by transitioning from fixed workout plans to dynamic, personalized strength curves that automatically adjust to user strength changes. The strength curves are continuously updated based on performance data, allowing the workout structure to adapt while maintaining consistency through systematic data collection and analysis.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system enables self-service by allowing users to automatically track their own strength progression through repeated baseline testing. The personalized strength curves self-adjust based on user performance data, eliminating the need for manual plan modifications while maintaining both structural consistency and adaptability.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If muscle fatigue is not properly measured, then workout intensity can be maintained, but accurate determination of ideal target weight becomes impossible

Engineering Contradiction:

Improveaccuracy of fatigue assessmentVSAvoidcomplexity of testing protocol

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system replaces complex mechanical fatigue measurement devices with a simplified digital approach using mobile applications and online platforms. Users input performance data through user-friendly interfaces, and algorithms automatically calculate fatigue metrics and generate personalized strength curves, achieving precise fatigue assessment without complex physical testing equipment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11253749B2Ideal target weight training recommendation system and method

Publication Date: 2022.02.22 RELIANCE CAPITAL ADVISORS LLC

AI summary

An ideal target weight training recommendation method ascertains a user's ideal target weight with a user-data algorithm that is operable to approximate an available weight value for a user to utilize for a given target weight training recommendation in addition to generating a rate in which a user's muscle fatigues. To do so, the user generates initial completed repetitions until the muscles fatigue. A baseline strength value is calculated with the initial completed repetitions and a baseline strength coefficient. A baseline strength value is created and used in calculating ideal target weight values. A y-intercept approximate functions involving user-selected desired target repetition values. The user manipulates a resistance structure associated with the ideal target weight values and said values are rounded to the nearest whole number. The user-selected desired target repetition values generate a second completed repetition values for subsequent sets for adjustment of a strength curve.