Energy Expenditure Calculation Using Multi-Device Data
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Solution Overview
Problem
Existing fitness tracking systems fail to accurately assess energy expenditure during physical activities due to cumbersome collection methods, inaccurate measurements, high power consumption, and inability to account for individual deviations, leading to decreased user motivation and engagement.
Innovation Solution
A system that receives data from connected devices using different operating protocols, identifies activity metrics, and compares them to a metric database to determine the best data source for calculating energy expenditure, incorporating user input and confidence weighting to provide accurate energy expenditure values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from multiple connected devices using different operating protocols, then measurement precision and reliability of energy expenditure calculation is improved, but device complexity and data processing burden increases
Solution Approach 1:
The patent introduces a gateway device as an intermediary that receives data from multiple connected devices using different operating protocols and translates/standardizes it into a common format. This gateway acts as a mediator between diverse data sources and the processing system, resolving protocol incompatibilities without requiring changes to the original devices. The gateway handles protocol conversion, data validation, and standardization, thereby improving measurement precision while containing device complexity within the gateway component rather than propagating it across the entire system.
Solution Approach 2:
The gateway device is designed with multi-functionality to handle multiple operating protocols simultaneously. It can receive, parse, and standardize data from various device types (fitness trackers, smartphones, wearables) using different protocols through a single unified interface. This universal approach allows the system to integrate diverse data sources without requiring separate processing paths for each device type, thereby improving measurement comprehensiveness while managing complexity through consolidation.
2Measurement precision
If activity metrics are continuously monitored and compared to metric database, then energy expenditure calculation accuracy is improved, but power consumption increases
Solution Approach 1:
The system implements periodic sampling of activity metrics rather than continuous monitoring. The gateway device collects data at predetermined time intervals from connected devices and performs comparisons with the metric database at these discrete moments. This periodic approach maintains measurement accuracy by capturing sufficient data points for reliable energy expenditure calculation while significantly reducing power consumption compared to continuous real-time monitoring. The system can adjust sampling frequency based on activity intensity and user needs.
Solution Approach 2:
The metric database is pre-populated with reference data, activity patterns, and comparison criteria before runtime operations. Activity metrics are compared against this pre-prepared database using predetermined algorithms and thresholds. This preliminary preparation of reference data and comparison logic enables accurate energy expenditure calculation without requiring complex real-time computations, thereby reducing power consumption during actual measurement and processing operations.
3Reliability
If confidence weighting is applied to activity metrics from different sources, then reliability of energy expenditure value is improved, but computational complexity increases
Solution Approach 1:
The system transforms multiple activity metric parameters from different sources into a unified confidence weighting parameter. Each data source is assigned a confidence weight based on predetermined criteria such as device reliability, data quality, and historical accuracy. The gateway device aggregates these weighted metrics and normalizes them into a single reliable energy expenditure value. This parameter transformation approach improves reliability by systematically accounting for data source variability while managing computational complexity through standardized weighting algorithms rather than complex multi-parameter processing.
Data Source
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AI summary
Aspects relate to athletic metric values from data received from different sources having different devices that employ different processes to calculate the values. Data may be received from a connected device that utilizes a first operating protocol, and be received by a device utilizing a second operating protocol. An activity metric may be identified from received data, and a determination may be made as to whether the activity metric is calculated using a best available data source. If it is determined that the received data represents a best available data source, the received data may be added to a metric database, the received data may be classified into an activity group, and an energy expenditure value may be calculated from the received data.