Wearable Sensor CRF Estimation via Activity Data
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Solution Overview
Problem
Current fitness tracking systems lack the ability to accurately and conveniently measure cardiorespiratory fitness (CRF) levels without specialized equipment, making it difficult for users to monitor improvements in their aerobic endurance and cardiovascular health over time.
Innovation Solution
A fitness tracking system that collects activity data from sensors and uses regression models to estimate CRF levels based on demographic and workout data, providing users with a CRF score and confidence rating displayed on a personal electronic device, allowing for continuous monitoring without the need for laboratory measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized equipment and laboratory protocols are used to measure VO2max, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent uses wearable sensors to capture movement data that copies or replicates the physiological responses measured in laboratory settings. Activity data from accelerometers and gyroscopes serves as a proxy copy of the actual VO2max measurement, allowing estimation without specialized laboratory equipment.
Solution Approach 2:
The patent replaces complex mechanical laboratory equipment (ergometers, gas analyzers, heart rate monitors) with electronic wearable sensors and computational algorithms. The mechanical measurement system is substituted with an electronic data collection and processing system that estimates VO2max from movement patterns.
2Measurement precision
If specialized equipment is used for CRF measurement, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The system allows users to self-monitor their CRF levels using wearable devices they already possess or can easily obtain. Users collect their own activity data through daily wear of the sensor, eliminating the need to visit laboratories or work with specialized equipment operators.
Solution Approach 2:
The wearable sensor serves multiple functions: it tracks general physical activity, monitors exercise workouts, and estimates VO2max/CRF levels. This multi-functional device replaces multiple specialized laboratory instruments, making the measurement accessible to general users rather than requiring dedicated CRF testing equipment.
3Measurement precision
If laboratory-based CRF testing is performed repeatedly, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The wearable sensor continuously collects activity data during daily wear, enabling ongoing monitoring of CRF levels without interruption. Instead of periodic laboratory visits, the system provides continuous data accumulation that feeds into CRF estimation, maintaining measurement relevance without time loss.
Solution Approach 2:
The system performs preliminary data collection and processing continuously in the background, preparing the activity data for CRF estimation before formal analysis is needed. This preliminary accumulation of movement data ensures that when CRF levels need to be assessed, the computational work has already been partially completed, reducing the time required for actual measurement and analysis.
Data Source
AI summary
A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model.