Multi-Sensor Event Correlation for Motion Capture Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current motion capture systems are inefficient in processing and storing event-related data, as they capture and store large amounts of non-relevant information, lack intelligent event confirmation using multiple sensors and social media data, and fail to automatically trim video to upload only pertinent event segments, leading to increased power, bandwidth, and memory requirements. Additionally, they cannot monitor rotational accelerations or velocities, and do not facilitate data mining for event patterns or cumulative impacts.
Innovation Solution
A multi-sensor event detection and tagging system that analyzes data from various sensors and non-sensor sources to generate concise event videos, integrates with multiple sensors to save event data even if others do not detect the event, and automatically generates tags based on analysis, enabling intelligent selection and synchronization of event videos, and allows for real-time alteration of camera parameters and playback settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion capture systems capture and store large amounts of data to ensure complete event coverage, then event detection reliability is improved, but storage requirements and processing time increase significantly
Solution Approach 1:
The system extracts only the relevant event segments from continuous motion capture data streams. Event detection algorithms identify specific temporal boundaries of events, and only these extracted segments are stored and processed further, discarding the vast majority of non-event data.
Solution Approach 2:
The system performs preliminary event detection and segmentation before final storage and analysis. Motion capture data is continuously monitored in real-time, and event boundaries are predetermined through detection algorithms, allowing the system to prepare and organize only necessary data segments for storage.
2Loss of information
If motion capture systems upload complete video and sensor data for thorough event analysis, then analysis completeness is improved, but bandwidth consumption and upload time increase
Solution Approach 1:
The system extracts and uploads only the pertinent event segments from complete video and sensor data. By identifying temporal boundaries of events, the system separates relevant event data from unrelated portions, transmitting only the extracted event segments to reduce bandwidth consumption while preserving essential analysis information.
3Device complexity
If motion capture systems use single sensor detection to simplify system complexity, then device complexity is reduced, but event detection accuracy decreases due to false positives
Solution Approach 1:
The system merges data from multiple disparate sensors including motion capture sensors, video cameras, and social media data sources. By combining these diverse data streams, the system cross-validates event detections, reducing false positives while maintaining manageable system complexity through integrated processing.
Solution Approach 2:
The system uses an intermediary event correlation module that reconciles data from multiple sensors and sources. This intermediary layer processes and harmonizes inputs from disparate sensors, enabling accurate event confirmation without requiring direct complex interconnections between all sensor components.
4Loss of information
If motion capture systems process and store all captured data for comprehensive analysis, then data mining potential is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and retains only event-related data segments for storage and analysis, discarding non-event portions. This extraction approach preserves the essential information needed for data mining patterns and trends while dramatically reducing the total data volume requiring processing and storage.
Solution Approach 2:
The system performs preliminary filtering and organization of motion capture data into event-based segments before storage. By pre-organizing data according to event boundaries and characteristics, the system prepares data for efficient future analysis and pattern recognition, reducing processing time when performing data mining operations.
Data Source
AI summary
A sensor event detection and tagging system that analyzes data from multiple sensors to detect an event and to automatically select or generate tags for the event. Sensors may include for example a motion capture sensor and one or more additional sensors that measure values such as temperature, humidity, wind or elevation. Tags and event detection may be performed by a microprocessor associated with or integrated with the sensors, or by a computer that receives data from the microprocessor. Tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags.