Athletic Event Detection via Distributed Activity Data Clustering
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Solution Overview
Problem
Athletes participating in organized events often lack comprehensive performance comparison data and receive limited information and support, as race data is typically only maintained and shared by event organizers, restricting access to detailed performance analysis and relevant advertisements or motivational messages.
Innovation Solution
A system and method that utilize activity monitoring devices to collect and process athletic data, cluster it based on defined parameters, and identify organized athletic events, allowing for the transmission of messages and statistical information to participants, including geo-position devices and a database to store and analyze the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If event organizers maintain and share race data, then athletes can receive performance information, but the information is incomplete or lacking in detail and access is restricted
Solution Approach 1:
The patent combines data from multiple independent activity monitoring devices worn by different athletes into a unified dataset. By merging these distributed data sources, the system reconstructs comprehensive race information without relying on a single centralized data collection point, thereby improving data completeness while avoiding the complexity of building an extensive organized data collection infrastructure.
Solution Approach 2:
Each athlete's activity monitoring device independently collects and stores its own performance data during the event. The devices automatically record metrics such as location, speed, and time without requiring external intervention. This self-service approach allows the system to gather comprehensive data from all participants simultaneously, eliminating the need for complex organized data collection systems while ensuring data completeness.
2Loss of information
If activity data from multiple participants is collected and analyzed, then comprehensive performance comparison is enabled, but the system complexity increases
Solution Approach 1:
The patent segments the data processing task by assigning analysis functions to individual activity monitoring devices rather than requiring a centralized complex processing system. Each device independently processes its own data and identifies events based on pre-defined criteria, dividing the overall complex analysis task into simpler, distributed units that can operate autonomously.
Solution Approach 2:
The system performs preliminary data filtering and event identification directly on the activity monitoring devices before data transmission. By pre-processing the data to identify organized athletic events and extract relevant performance metrics at the source, the system reduces the complexity of subsequent centralized data processing while ensuring comprehensive performance comparison data is captured.
3Loss of information
If detailed performance analysis is provided to athletes, then athlete engagement improves, but data collection and processing requirements increase
Solution Approach 1:
The patent extracts only the essential performance metrics and event identification data from the complete activity dataset. Rather than transmitting or processing all raw data, the system selectively extracts relevant information such as event participation status, key performance indicators, and comparison metrics, thereby providing detailed performance analysis while minimizing the quantity of data that must be collected and processed.
Solution Approach 2:
The system collects slightly more data than the minimum required by capturing comprehensive activity metrics from all participants, but then applies selective extraction to provide the specific detailed performance analysis needed. This approach ensures sufficient data is gathered to enable thorough performance comparison while avoiding the burden of processing excessive unnecessary data volumes.
Data Source
AI summary
A method of identifying the occurrence of one or more organized athletic events including a plurality of participants is disclosed. The method includes receiving athletic activity data for the participants from a plurality of athletic activity monitoring devices carried by the participants. The received athletic activity data defines workouts performed by the participants. The method further includes defining a plurality of parameters for the athletic activity data, wherein the parameters define common athletic activity data for the participants of each of the organized athletic events. In addition, the method includes clustering the athletic activity data into one or more clusters of workouts based on the defined parameters for the athletic activity data. Furthermore, the method includes identifying the one or more organized athletic events based on the one or more clusters of workouts.