Exercise Parameter Estimation Using Data Reliability Filtering
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Solution Overview
Problem
Existing exercise monitoring devices often provide inaccurate exercise parameter estimates due to unreliable exercise data, which can occur when the measuring device is not properly fastened to the skin or operates abnormally, leading to imprecision in fitness or health guidance.
Innovation Solution
A method that determines the reliability of acquired exercise data by setting a criterion set and using a judgement parameter set to confirm data reliability, ensuring precise estimation of exercise parameters such as VO2max or FTP, by analyzing internal and external workload data consistency and correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If exercise data is acquired continuously during workout, then productivity of fitness monitoring is improved, but reliability of exercise data deteriorates due to device instability or abnormal operation
Solution Approach 1:
The system performs preliminary actions by establishing multiple criterion sets before data acquisition, including device stability criteria, signal quality criteria, and physiological parameter consistency criteria. These pre-defined criteria enable automatic validation of exercise data during continuous monitoring, ensuring reliability is maintained throughout the workout without interrupting productivity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring data quality metrics and comparing them against criterion sets. When data fails to meet criteria, the system provides feedback through automatic re-evaluation, alternative measurement methods, or user notifications, thereby maintaining both continuous monitoring productivity and data reliability through iterative validation.
2Reliability
If multiple criterion sets are established to validate exercise data, then reliability of exercise parameter estimation is improved, but device complexity increases due to additional validation mechanisms
Solution Approach 1:
The validation system is segmented into multiple independent criterion sets, each focusing on specific aspects of data quality (device stability, signal quality, physiological consistency). This segmentation allows the complex validation task to be divided into manageable, modular criteria that can be evaluated independently, reducing overall system complexity while maintaining comprehensive validation for reliable exercise parameter estimation.
3Manufacturing precision
If exercise data validation is performed using judgement parameter set, then manufacturing precision of data quality control is improved, but ease of operation deteriorates due to automated algorithm complexity
Solution Approach 1:
The system implements self-service by enabling automatic data validation and quality assessment through pre-configured criterion sets and judgment parameters. The automated algorithm independently evaluates exercise data against multiple criteria without requiring manual intervention, thereby achieving high data quality control precision while maintaining ease of operation through autonomous functionality.
Data Source
AI summary
The embodiments of the disclosure provide a method for determining an exercise parameter if the exercise data is reliable. The exercise data is reliable if the criterion set is met by the exercise data. The method comprises: acquiring exercise data; confirming whether a criterion set is met by a judgement parameter set determined based on the exercise data or not; and using the exercise data to determine an estimation of the exercise parameter if the criterion set is met by the judgement parameter set.