Strength Exercise Machine Power Feedback for Adaptive Workload
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Solution Overview
Problem
Existing strength training methods using exercise machines fail to effectively train user power generation while avoiding excessive fatigue, unnecessary strain, and injury risk, necessitating personal trainers or automated suggestions for correct exercise performance.
Innovation Solution
A training method and exercise machine that utilize a data processing unit to set workload, detect power peaks, store reference values, and adjust settings based on peak comparisons to optimize power training, incorporating a motor, memory unit, and display module for real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time power monitoring and dynamic workload adjustment are implemented, then training effectiveness and safety are improved, but device complexity increases
Solution Approach 1:
The system continuously monitors user power output during exercise repetitions and provides real-time feedback by comparing actual power values against target power values. This feedback mechanism enables dynamic adjustment of workload to maintain optimal training intensity while preventing excessive fatigue or injury risk, directly resolving the contradiction between safety and system complexity.
Solution Approach 2:
The exercise machine automatically adjusts workload parameters based on real-time power monitoring without requiring external intervention from a personal trainer. The system self-regulates training intensity by comparing detected power peaks against stored reference values and automatically modifying subsequent workout parameters, thereby improving safety while minimizing the need for complex external control systems.
2Measurement precision
If continuous power monitoring at each repetition is performed, then training precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-storing reference maximum power values and target power values before the actual training session begins. During the workout, the system only needs to compare real-time power measurements against these pre-established thresholds rather than performing complex calculations, thereby maintaining high measurement precision while minimizing time loss during the actual exercise repetitions.
3Productivity
If dynamic workload adjustment based on power peaks is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic workload adjustment by continuously adapting the target power values and workload parameters based on real-time detection of power peaks during exercise repetitions. This dynamic approach optimizes training efficiency by ensuring each repetition occurs at the optimal intensity level while using straightforward comparison logic rather than complex control algorithms, thereby improving productivity with minimal increase in device complexity.
Data Source
AI summary
A training method on a strength exercise machine is usable by a user to perform a strength exercise, as a series of repetitions, for training the power generable by the user in at least one first workload regimen. In case a determined or detected power peak value is either higher than or equal to a stored maximum-power reference value, the steps of updating the stored target power value as a function of the generated power peak value determined or detected, updating the stored maximum-power reference value to the determined or detected power peak value, and resetting a counter variable value are performed. In case the determined or detected power peak value is lower than the stored reference maximum power value, a step of incrementing the stored counter variable value is performed. The method is performed for each further repetition of the first workout series.