Exercise Machine Repetition Detection via Extremum Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Monitoring repetitions in exercise routines is challenging due to variations in user movement, making it difficult to accurately detect and control exercise machine resistance.
Innovation Solution
A system and method for detecting the first repetition and phases of a repetition in an exercise, using a stream of measurements from exercise machine sensors to match extremum parameters with user-specific signatures, allowing for real-time adjustment of resistance and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional repetition monitoring methods are used, then the system is simple to operate, but measurement precision deteriorates due to variation in user movement
Solution Approach 1:
The system performs preliminary characterization of the stream of measurements to establish user-specific signatures and extremum parameters before actual repetition detection. This preliminary action creates a reference framework that enables accurate detection despite movement variations, resolving the contradiction between precision and complexity by preparing the detection criteria in advance.
Solution Approach 2:
The system uses feedback by comparing detected extremum parameters against previously determined user-specific signatures. This feedback mechanism allows the system to adapt to individual user movement patterns, improving measurement precision while maintaining manageable complexity through intelligent comparison rather than complex real-time analysis.
2Measurement precision
If user-specific signatures are used for detection, then measurement precision improves, but ease of operation worsens due to increased setup requirements
Solution Approach 1:
The system performs self-service by automatically characterizing the stream of measurements and determining user-specific signatures without requiring manual input or configuration from the user. The extremum parameters and signatures are extracted automatically during normal operation, maintaining ease of operation while achieving high measurement precision through adaptive user-specific detection.
Data Source
AI summary
Controlling an exercise machine includes receiving a stream of measurements of extension of a component of an exercise machine. It further includes characterizing the stream of measurements including detecting at least one extremum having at least one extremum parameter. It further includes matching the extremum parameter with a previously determined signature associated with a user. It further includes changing an output of the exercise machine based at least in part on the match.