Strength Calibration Using Seed Movements for Safe Weight Selection
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Solution Overview
Problem
Strength training users often lack knowledge about assessing their own strength, leading to inappropriate weight selection and potential injury or discouragement due to lack of progress.
Innovation Solution
A system for strength determination and calibration that estimates a user's weight capability based on multi-dimensional performance analysis of historical exercise movements and population-level data, using isokinetic seed movements to determine a one rep maximum (1eRM) and suggest appropriate weights for subsequent exercises, incorporating a digital strength training machine with motor torque control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users select weights independently without guidance, then users have autonomy in exercise selection, but users risk injury or discouragement due to inappropriate weight selection
Solution Approach 1:
The system performs preliminary strength calibration before the user begins their workout routine. The calibration process assesses the user's strength levels across multiple muscle groups and movements, establishing a baseline that the system uses to automatically suggest appropriate weights. This preliminary assessment prevents injury by ensuring users start with weights matched to their actual capability, eliminating the need for users to guess or independently select weights.
Solution Approach 2:
The system continuously monitors user performance during exercises and provides real-time feedback on form, effort, and progression. Based on this feedback, the system dynamically adjusts weight recommendations and provides guidance on proper technique. This closed-loop feedback mechanism ensures safety by alerting users to potential form errors and preventing injury while progressively challenging users to improve.
2Measurement precision
If traditional strength testing methods are used, then accurate strength measurement is achieved, but the testing process is time-consuming and requires user effort that may cause injury
Solution Approach 1:
Instead of requiring users to perform maximal strength tests to failure (excessive action), the system uses sub-maximal calibration exercises that assess strength potential without pushing users to their limits. The calibration process uses multiple movements at moderate intensity levels, and the system's algorithms extrapolate maximum strength capability from this partial effort data. This approach achieves accurate strength measurement while significantly reducing the time and physical stress required compared to traditional maximal testing protocols.
3Adaptability or versatility
If generic workout programs are provided, then program simplicity is maintained, but user progress is limited due to lack of personalization
Solution Approach 1:
The system tailors workout programs to each user's specific strength profile, identifying strong and weak muscle groups based on calibration data. Rather than applying a uniform program, the system customizes exercise selection, intensity, and progression for each individual user's needs. This localized personalization ensures that each user receives a program optimized for their specific physiological characteristics, maximizing progress while the system automatically manages the complexity of tracking and adjustment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides a safe and effective method for users to determine their strength levels, reducing the risk of injury and improving user experience by suggesting appropriate weights for various exercises, allowing for progressive weight mode and real-time adjustments.
Implementation Method 1
a motor configured to apply a controlled torque to a flywheel
Data Source
AI summary
A first set of performance information pertaining to a previous performance of a first exercise movement is received, the first set of performance information comprising a first weight, a first velocity, and a first range of motion. Target parameters for a target exercise movement based at least in part on the first set of performance information is predicted, wherein the target exercise movement is different from the first exercise movement. An exercise machine is configured to facilitate performing of the target exercise movement based at least in part on the predicted target parameters.