Exercise Machine Struggle Detection With Repetitions-in-Reserve Prediction
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Solution Overview
Problem
Existing exercise machines lack the ability to accurately detect when a user is struggling during strength training, which can lead to physical exhaustion and failure, posing safety risks.
Innovation Solution
Implementing a system that uses data science and heuristics to predict and classify upcoming repetitions, determining the 'repetitions in reserve' by analyzing user performance on isokinetic seed movements, and adjusting resistance levels to prevent physical failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the exercise machine provides fixed resistance levels, then the device complexity is low, but the user safety deteriorates due to inability to detect struggle and prevent physical failure
Solution Approach 1:
The system continuously monitors user performance metrics (velocity, power, range of motion) during exercise repetitions and uses this feedback to detect struggle conditions. The processor compares real-time performance against expected performance patterns to identify when the user is approaching physical failure, enabling dynamic safety interventions.
Solution Approach 2:
The system predicts upcoming repetition performance based on historical data and current exercise patterns before the user actually performs them. By classifying predicted repetitions and identifying struggle conditions in advance, the system can prevent physical failure before it occurs, rather than reacting after exhaustion sets in.
2Reliability
If the system dynamically adjusts resistance levels in real-time, then user safety improves, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The exercise machine autonomously monitors its own operation and the user's performance without requiring external supervision. The processor automatically detects struggle conditions based on sensor data and adjusts resistance levels or provides alerts without needing a trainer or spotter, making the system self-regulating for safety.
Solution Approach 2:
The sensor system serves multiple functions: it monitors exercise form, measures performance metrics for tracking progress, detects struggle conditions for safety, and provides feedback for technique improvement. This multi-functionality reduces the need for separate specialized systems while achieving comprehensive safety monitoring.
3Measurement precision
If the system uses data science and heuristics to predict upcoming repetitions, then the measurement precision of struggle detection improves, but the device complexity increases due to advanced algorithms
Solution Approach 1:
The system performs preliminary classification of upcoming repetitions by analyzing patterns from previous repetitions and exercise data. This predictive classification identifies which future repetitions are likely to be struggle repetitions, allowing the system to prepare appropriate safety interventions in advance with high precision.
Solution Approach 2:
The system analyzes more performance parameters and historical data than strictly necessary for basic monitoring, using comprehensive data collection to improve prediction accuracy. By examining velocity, power, range of motion, and temporal patterns across multiple repetitions, the system achieves superior struggle detection precision through excessive measurement.
Data Source
AI summary
Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.