Exercise Database Server Matching Metric Categories for Data Completeness
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Solution Overview
Problem
Existing systems face challenges in accurately and efficiently assigning exercise performance data to an individual's workout events, especially when not all necessary data is collected by sensors during the workout, leading to incomplete performance metrics.
Innovation Solution
A server system that retrieves and assigns performance data from an exercise database by matching received data with analogous entries based on metric categories, allowing for the selection and association of missing data points, such as heart rate or caloric burn, even if not measured during the exercise event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensors are worn by the exerciser to measure performance attributes, then performance data can be collected, but not all necessary data is collected by sensors during the workout leading to incomplete performance metrics
Solution Approach 1:
The patent introduces an exercise database as an intermediary source that provides performance data not directly collected by sensors. The database serves as a mediator between the sensor data and the complete performance metrics, filling in gaps for attributes like heart rate or caloric burn that were not measured during the workout.
Solution Approach 2:
The exercise database stores performance data from previous workouts and exercise events in advance. This preliminary action allows the system to retrieve and apply relevant historical data when current sensor data is incomplete, enabling comprehensive performance analysis without requiring all sensors to be worn during each workout.
2Loss of information
If multiple sensors are used to measure all performance attributes, then data completeness improves, but device complexity and cost increase
Solution Approach 1:
The exercise database acts as an intermediary that provides supplementary performance data without requiring additional physical sensors. The database retrieves relevant information from historical records to complement sensor data, maintaining comprehensive metrics while reducing the number of required sensors during actual workouts.
Solution Approach 2:
The system creates a virtual copy of performance data by retrieving analogous entries from the exercise database. Instead of physically measuring all attributes with multiple sensors, the system copies relevant performance characteristics from historical workout records to supplement incomplete sensor data.
3Measurement precision
If all performance data is collected during workouts, then accuracy improves, but the need for continuous sensor wear increases
Solution Approach 1:
The exercise database serves as an intermediary that provides accurate performance data without requiring continuous sensor wear. The database retrieves precise metrics from historical workouts, allowing the system to maintain measurement accuracy while reducing the operational burden of continuous sensor usage during each exercise session.
Solution Approach 2:
Performance data is collected and stored in advance during previous workouts. This preliminary action enables the system to retrieve accurate performance characteristics when needed, eliminating the requirement for continuous sensor wear during each workout while maintaining data accuracy through historical records.
Data Source
AI summary
Techniques are provided for retrieving performance data from an exercise database. At a server device, data entries are stored in a database. Each of the exercise data entries comprises one or more performance metrics for a corresponding metric category. The server receives first performance data of an individual performing an exercise event and selects one of the exercise entries in the database as a selected exercise entry. The server determines a metric category for the first performance data and matches the metric category of the first performance data with a same metric category for the selected exercise entry. The server retrieves second performance data from the selected exercise data entry. The second performance data belongs to a metric category that does not match the metric category of the first performance data. The server assigns the second performance data as data associated with the exercise event.