Exercise Evaluation System Using Frequency-Score Time-Point Filtering
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Solution Overview
Problem
Existing exercise evaluation systems fail to effectively select and evaluate portions of exercises that represent the user's average performance, especially when sudden or deviant motions are present, and do not efficiently integrate and analyze multiple data points.
Innovation Solution
An exercise evaluation system that calculates frequency scores for individual time points, fixes these scores as total frequency scores, selects effective time points based on these scores, and evaluates exercises using index values at these points, while accounting for normal distribution of index values to filter out deviant motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exercise evaluation systems evaluate all time points during exercise, then comprehensive data collection is achieved, but sudden or deviant motions contaminate the average performance evaluation
Solution Approach 1:
The system performs preliminary actions by calculating frequency scores for all index values before evaluation, and pre-identifying effective time points where the total frequency score meets the threshold condition. This preliminary filtering ensures that when actual evaluation occurs, only clean data points representing average performance are used, eliminating contamination from sudden or deviant motions.
Solution Approach 2:
The system applies local quality by evaluating different time points differently based on their frequency score characteristics. Instead of uniform evaluation across all time points, it selectively evaluates only those time points where the total frequency score of all index values meets the threshold, ensuring that local data quality varies according to representativeness.
2Loss of information
If multiple index values are calculated and analyzed for each time point, then comprehensive exercise analysis is achieved, but processing complexity increases
Solution Approach 1:
The system merges multiple index value calculations by summing their frequency scores to produce a single total frequency score for each time point. This consolidation simplifies the decision-making process while retaining comprehensive information, as the threshold evaluation of the summed score implicitly considers all individual index values without requiring separate processing of each.
Solution Approach 2:
The system extracts only the essential information needed for evaluation by calculating frequency scores for multiple index values and then combining them into a single total frequency score metric. This extraction approach maintains data completeness while reducing processing complexity by focusing on the aggregated score rather than individually analyzing each index value.
3Measurement precision
If frequency scores are calculated and summed for all index values at each time point, then accurate effective time point selection is achieved, but calculation time increases
Solution Approach 1:
The system performs preliminary calculations of frequency scores for all index values and their sums before final evaluation. By pre-computing these values and storing them, the system avoids redundant calculations during the actual evaluation phase, thereby maintaining high selection accuracy while reducing overall processing time.
Data Source
AI summary
An electrical device includes a processor. The processor calculates sums of frequency scores, which are associated with the respective index values regarding exercises of a user and are calculated for individual time points, and fixes the sums of frequency scores as total frequency scores at the individual time points. The processor selects effective time points for evaluation of the exercises of the user, on the basis of the fixed total frequency scores. The processor evaluates the exercises of the user, on the basis of the index values at the selected effective time points.