Sensor Event Detection System for Motion Data Filtering
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Solution Overview
Problem
Existing motion capture systems fail to efficiently detect and differentiate various types of motion events, often storing and uploading large amounts of non-relevant data, lacking integration with multiple sensors, and not effectively filtering or correlating events with social media, leading to increased storage, power, and bandwidth requirements.
Innovation Solution
A sensor and media event detection system that utilizes motion capture sensors and other environmental sensors to intelligently synchronize and transfer concise event videos, filters by metrics, and integrates with multiple sensors to confirm events, even if other sensors do not detect them, allowing for event-based viewing and low power transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion capture systems store and upload all captured data, then complete motion data is preserved, but storage requirements and upload times increase significantly
Solution Approach 1:
The system extracts only the relevant portions of motion data that correspond to detected events, separating them from the bulk of non-event data. This is achieved by using event detection algorithms to identify specific time intervals where events occur, then extracting only those segments for storage and upload, thereby reducing storage requirements while preserving all pertinent motion information.
Solution Approach 2:
The system performs preliminary event detection and data filtering before storage and upload operations. By pre-identifying event segments and marking them for retention, the system prepares the data in advance, ensuring that only necessary data is stored and transmitted, thus reducing overall storage requirements and upload times.
2Reliability
If motion capture systems store and upload all captured data, then complete motion data is preserved, but upload times increase significantly
Solution Approach 1:
The system extracts only the relevant portions of motion data that correspond to detected events, separating them from the bulk of non-event data. This is achieved by using event detection algorithms to identify specific time intervals where events occur, then extracting only those segments for storage and upload, thereby reducing storage requirements while preserving all pertinent motion information.
Solution Approach 2:
The system performs preliminary event detection and data filtering before storage and upload operations. By pre-identifying event segments and marking them for retention, the system prepares the data in advance, ensuring that only necessary data is stored and transmitted, thus reducing overall storage requirements and upload times.
3Measurement precision
If multiple sensors are integrated to confirm events, then event detection accuracy is enhanced, but device complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (accelerometers, gyroscopes, magnetometers, barometers, cameras) into a unified event detection framework. By combining these sensors and coordinating their detection algorithms, the system achieves enhanced event detection accuracy through multi-sensor confirmation, where an event is validated when detected by multiple sensors, reducing false positives while managing complexity through integrated processing.
4Quantity of substance
If event-based filtering is applied to reduce data, then storage requirements are reduced, but risk of eliminating relevant data increases
Solution Approach 1:
The system employs feedback mechanisms where event detection results from multiple sensors are continuously monitored and used to adjust filtering parameters. The multi-sensor confirmation process provides feedback loops that verify event validity before filtering, ensuring that only genuinely relevant data is eliminated while preserving all significant motion events, thus maintaining data completeness while reducing storage requirements.
Data Source
AI summary
Enables detection of events using motion capture sensors and potentially other sensors electromagnetic field, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate sensors to confirm and post events, differentiate similar types of motion events to determine the type of equipment or activity or quality of the event, such proficiency. Enables motion capture data and other sensor data to be utilized to curate text, images, video, sound and post the results to social networks, for example in a dedicated feed. Embodiments of the system also may post or filter to social media sites using any other filter besides location and time and the text in the social media posts for example. May use motion or other sensor data to define and event, eliminate false positive events, post true events, and/or correlate the events with social media to confirm the events, or post the events in a particular channel.