Activity Calibration Parameter Adjustment for Stride Variance
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Solution Overview
Problem
There is a disparity between user-perceived and device-calculated distances in activity tracking due to variance in stride length and unaccounted elevation changes, leading to inaccurate activity measurements in systems that rely on traditional calibration parameters.
Innovation Solution
A method and system for determining, recommending, and applying a calibration parameter for activity measurement, specifically stride length, based on user-specific data and GPS data, allowing user adjustment through an interactive interface to ensure accurate distance calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration parameters are used for activity measurement, then the device can provide distance calculations, but the measurements are inaccurate due to variance in user stride length and unaccounted elevation changes
Solution Approach 1:
The system performs preliminary calibration by collecting user-specific activity data (steps, distance, elevation) during initial activities, then uses this data to calculate and store customized calibration parameters before they are needed for accurate distance measurements. This preliminary data collection and analysis enables the system to adapt to individual user characteristics in advance.
Solution Approach 2:
The system dynamically changes calibration parameters based on collected user-specific data. Instead of using fixed default values, the calibration parameters (such as stride length and elevation factors) are adjusted according to each user's actual performance data, transforming the measurement system from a generic to a personalized state for improved accuracy.
2Measurement precision
If user-specific calibration parameters are implemented, then measurement accuracy improves, but the system complexity increases due to data collection and user interaction requirements
Solution Approach 1:
The system uses a multi-functional approach where the same data collection mechanisms serve multiple purposes: collecting activity data for health tracking, gathering calibration information for accuracy improvement, and storing user preferences for personalized experience. This universal data collection strategy reduces the need for separate dedicated calibration systems.
Solution Approach 2:
The system performs self-calibration by automatically collecting user activity data, analyzing performance patterns, and generating customized calibration parameters without requiring manual user intervention. The device serves itself by using its own collected data to improve its measurement accuracy, reducing the burden on users while maintaining simplicity.
Data Source
AI summary
Apparatus and methods are provided for determining, recommending, and applying a calibration parameter to collected activity data. In one embodiment, calibration parameter is estimated based on physical aspects of the user and automatically applied to collected data. In another embodiment, the calibration parameter is determined based on secondary data which is more precise than the data which is collected. The calibration factor based on the more precise data may comprise a recommended calibration factor, yet the user may be enabled to select any calibration factor he/she prefers via an interactive display. In one specific variant, the activity comprises a walk or run activity of the user, and the calibration parameter comprises the user's specific stride length. In another variant, the user selects a calibration factor by reviewing a list of previous activity against that same activity after calibration given a particular calibration factor is applied.