Impact Detection via Multi-Camera Kinematic Analysis
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Solution Overview
Problem
Current impact analysis in fields like sports and traffic is limited by the lack of contextual information, often relying solely on manual analysis of specific events and sensor data, which fails to provide a comprehensive understanding of the circumstances surrounding impacts and their consequences.
Innovation Solution
The integration of video data from multiple cameras with sensor data, using techniques like feature and pose detection, triangulation, and machine learning to analyze kinematic data and contextual parameters, enabling the identification and analysis of impacts and their potential consequences over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis of specific events and sensor data is used, then analysis simplicity is maintained, but comprehensiveness of impact analysis deteriorates due to lack of contextual information
Solution Approach 1:
The patent combines multiple data sources including video data from multiple cameras, sensor data from devices worn by individuals, and kinematic data to create a comprehensive impact analysis system. This merging of diverse data types provides rich contextual information while maintaining automated analysis capability.
Solution Approach 2:
The system performs multiple functions including detecting impacts, analyzing kinematic data, determining severity, and providing comprehensive context about circumstances surrounding impacts. This multi-functional approach replaces manual analysis while enhancing information completeness.
2Loss of information
If video data from multiple cameras and sensor data integration is implemented, then comprehensiveness of impact analysis is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: video data processing, sensor data processing, kinematic data analysis, and impact detection. Each component handles specific data types and can be processed independently, then integrated to form the complete impact analysis.
Solution Approach 2:
The patent uses an intermediary computing system that receives and processes data from multiple cameras and sensor devices. This intermediary system integrates the data streams and performs the complex analysis, isolating the complexity from the end users while providing comprehensive results.
3Productivity
If automated analysis using video and sensor data integration is used, then productivity of impact analysis is improved, but measurement precision may deteriorate due to data correlation challenges
Solution Approach 1:
The system incorporates feedback mechanisms where detected features and poses are tracked over time, and data from multiple cameras is correlated to improve accuracy. The kinematic analysis uses feedback from previous frame data to enhance measurement precision while maintaining automated processing.
Solution Approach 2:
The patent triangulates participant motion in three-dimensional space by correlating data from multiple cameras viewing the same area from different angles. This dimensional approach improves measurement precision by providing multiple perspectives on the same events.
Data Source
AI summary
A kinematic analysis system captures and records participant motion via a plurality of video cameras. A participant feature and participant pose are identified in a frame of video data. The feature and pose are correlated across a plurality of frames. A three-dimensional path of the participant is determined based on correlating the feature and pose across the plurality of frames. A potential impact is identified based on analysis of the participant's path.