Workout Autodetection With Motion-Stream Deduplication
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Solution Overview
Problem
Existing activity tracking systems require intense user engagement and often rely on multiple devices or applications, lacking a seamless integration of data from various sources to provide an accurate and comprehensive snapshot of an individual's activity levels.
Innovation Solution
A portable electronic device with a motion sensor and processor that automatically detects and categorizes activities by processing motion data streams from both operating system and third-party platforms, merging and de-duplicating data to provide a unified activity summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple devices or applications are used to track activities, then comprehensive tracking coverage is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent merges activity tracking functionality from multiple sources (operating system activity streams, third-party fitness applications, and manual inputs) into a single unified tracking system. The system consolidates data from different devices and applications into one comprehensive activity summary, eliminating the need for users to manage multiple separate tracking systems while maintaining comprehensive coverage.
Solution Approach 2:
The system provides universal activity tracking by accepting data from multiple sources including operating system activity streams, third-party fitness applications, and manual inputs. This multi-functional approach allows a single system to handle various types of activity data from different sources, making it adaptable to different user needs and data sources without requiring multiple specialized devices.
2Measurement precision
If activity data from multiple sources is aggregated, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct stages: collecting activity streams from multiple sources, normalizing the data to a common format, de-duplicating overlapping entries, and generating a unified activity summary. This segmentation allows complex data from multiple sources to be processed systematically through manageable steps, improving accuracy while organizing complexity into structured processing phases.
Solution Approach 2:
The system uses an intermediary processing layer that normalizes and reconciles data from multiple sources before generating the final activity summary. This intermediary processing layer handles the complexity of data aggregation, normalization, and de-duplication automatically, allowing accurate measurement without exposing the user to processing complexity.
3Ease of operation
If automated activity detection is implemented, then ease of operation is improved, but measurement precision may deteriorate due to lack of user input
Solution Approach 1:
The system implements partial automation by automatically processing activity data from multiple sources while allowing users to provide supplementary inputs when needed. Rather than fully automated detection that might miss nuances, the system combines automatic data aggregation with optional user inputs to refine activity classifications, achieving both ease of operation and measurement precision through partial rather than complete automation.
Data Source
AI summary
Devices, systems, and methods can be used including receiving motion data, categorizing the motion data into portions of a minute that indicate activity or a workout, and automatically determining an accurate number of active minutes for an individual. Multiple data streams may be analyzed and de-duplicated, such that an accurate metric may be computed and reported to an individual.