Motion Event Video Integration System for Storage Reduction
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Solution Overview
Problem
Current motion capture systems face challenges in efficiently processing and storing large amounts of data, particularly in capturing and storing non-event related data, which increases power, bandwidth, and memory requirements, and are limited in detecting rotational accelerations and cumulative impacts, leading to inaccurate analysis and lack of seamless integration with mobile devices for real-time monitoring and data mining.
Innovation Solution
A video and motion event integration system that uses motion capture sensors to intelligently synchronize and transfer concise event videos, selects the best camera footage, alters camera parameters in real-time, and integrates with multiple sensors to analyze and compare movement data, enabling recognition of events like concussions and other physical motions, while reducing storage requirements and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion capture systems capture and store all motion data continuously, then complete motion records are available for analysis, but storage requirements, power consumption, and bandwidth usage increase significantly
Solution Approach 1:
The system extracts only the relevant portions of motion data that correspond to detected events, rather than storing all continuous motion data. Event detection algorithms identify specific time intervals where meaningful events occur, and only these segmented event data portions are stored, eliminating redundant non-event data from storage requirements
Solution Approach 2:
The continuous motion data stream is segmented into discrete event-based intervals. The system divides the continuous data flow into meaningful segments bounded by event detection points, allowing selective storage and processing of only those segments containing actual events of interest
2Reliability
If motion capture systems capture and transfer all video data, then complete event coverage is achieved, but upload speed decreases and storage requirements increase
Solution Approach 1:
The system extracts and transfers only video segments corresponding to detected motion events, rather than uploading complete continuous video streams. This selective extraction maintains all necessary event information while dramatically reducing the total data volume that requires transmission and storage
Solution Approach 2:
The system performs preliminary event detection and video segmentation before the upload process. By pre-identifying event boundaries and extracting relevant video portions in advance, the system prepares optimized data packages for efficient transmission, avoiding the need to transfer entire video files
3Device complexity
If motion capture systems monitor only linear acceleration, then system complexity is reduced, but detection accuracy for rotational events like concussions is insufficient
Solution Approach 1:
The system merges multiple sensor types including linear accelerometers, rotational gyroscopes, and magnetometers into an integrated inertial measurement unit. This combination enables comprehensive detection of both linear and rotational motion components, providing accurate three-dimensional orientation and acceleration data for complex events like concussions
Solution Approach 2:
The system uses a composite sensing approach that integrates data from multiple sensor modalities (accelerometers, gyroscopes, magnetometers) to create a unified motion detection capability that exceeds the capabilities of individual sensors alone
4Loss of time
If motion capture systems process data in real-time on mobile devices, then immediate event detection is achieved, but power consumption and processing load increase
Solution Approach 1:
The system uses periodic sampling of motion data at optimized intervals rather than continuous processing. Event detection algorithms check for events at regular intervals, and data processing is triggered periodically or event-driven, reducing overall computational load while maintaining timely event detection capability
Solution Approach 2:
The motion capture system performs self-service by implementing efficient on-device event detection algorithms that process data locally without requiring constant cloud connectivity. The system autonomously identifies events and manages data processing tasks, reducing power consumption associated with continuous cloud communication
Data Source
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AI summary
Enables intelligent synchronization and transfer of generally concise event videos synchronized with motion data from motion capture sensor(s) coupled with a user or piece of equipment. Greatly saves storage and increases upload speed by uploading event videos and avoiding upload of non-pertinent portions of large videos. Provides intelligent selection of multiple videos from multiple cameras covering an event at a given time, for example selecting one with least shake. Enables near real-time alteration of camera parameters during an event determined by the motion capture sensor, and alteration of playback parameters and special effects for synchronized event videos. Creates highlight reels filtered by metrics and can sort by metric. Integrates with multiple sensors to save event data even if other sensors do not detect the event. Also enables analysis or comparison of movement associated with the same user, other user, historical user or group of users.