Muscle Strength Training Load Specification via Spectral Analysis
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Solution Overview
Problem
Existing muscle strength training devices struggle to set appropriate loads for users with varying muscle growth responses, as they often apply the same training load to users with the same muscle mass index, failing to account for individual differences in muscle growth rates.
Innovation Solution
A load specifying method using a vector neural network-based machine learning model that analyzes time-series waveform data from users performing muscle strength training, comparing known feature spectra to target spectra to determine the similarity and identify optimal candidate loads for each user based on predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the same training load is set for users with the same muscle mass index value, then the training program is simple to generate, but the load may not be appropriate for individual users with varying muscle growth responses
Solution Approach 1:
The system changes the parameter of load specification from a single value based on muscle mass index to a time-varying load based on spectral characteristics of muscle response. The load is dynamically adjusted according to the extracted spectral features from ultrasonic waveform data, allowing each user to receive personalized load recommendations that adapt to their individual muscle growth responses.
Solution Approach 2:
The system replaces the simple mechanical calculation of load based on muscle mass index with a complex information processing system using spectral analysis and machine learning. The ultrasonic waveform data is transformed into spectral features through Fast Fourier Transform and processed by a trained neural network to determine optimal loads, substituting straightforward mechanical computation with intelligent data-driven decision making.
2Productivity
If a simple extrapolation line method is used to determine load and training次数, then the calculation is fast and simple, but it cannot account for individual differences in muscle growth rates
Solution Approach 1:
The system performs preliminary extraction of spectral features from ultrasonic waveform data before determining the training load. By pre-processing the waveform data to obtain spectral characteristics and comparing them against stored reference data, the system prepares comprehensive user-specific profiles that enable precise load specification while maintaining efficient calculation through the pre-organized reference database.
Solution Approach 2:
The system introduces spectral features as an intermediary between the raw ultrasonic waveform data and the final load determination. The spectral features serve as a bridge that captures essential information about muscle response characteristics, allowing the system to translate complex waveform data into meaningful load recommendations through comparison with reference spectral data.
Data Source
AI summary
A load specifying method for a muscle strength training device includes: acquiring a known feature spectrum for each of a plurality of pieces of time-series waveform data; acquiring target time-series waveform data; acquiring a target feature spectrum for each of a plurality of pieces of target time-series waveform data; and specifying a spectrum similarity satisfying a predetermined extraction condition and specifying a candidate load corresponding to the target time-series waveform data as a calculation source of the specified spectrum similarity.