Multi-Sensor Event Detection System Using Preliminary Motion Analysis
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Solution Overview
Problem
Existing motion capture systems are inefficient in processing and storing event-related data, as they capture large amounts of non-pertinent information, lack robustness in detecting rotational accelerations and velocities, and do not effectively integrate with multiple sensors or social media data, leading to increased power, bandwidth, and memory requirements, and limited use in monitoring events like concussions.
Innovation Solution
A multi-sensor event detection system that analyzes and synchronizes event data from various sensors and non-sensor sources, such as social media, to generate concise event videos, trim non-pertinent data, and integrate with multiple sensors, enabling intelligent analysis, real-time camera parameter adjustments, and event-based viewing, while using portable wireless motion capture elements and RFID tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion capture systems capture continuous video and motion data, then complete event information is recorded, but storage requirements and upload bandwidth increase significantly
Solution Approach 1:
The system performs preliminary analysis of motion data to detect events before video upload, trimming video clips to only include segments containing detected events. This preliminary filtering action reduces the volume of data that needs to be stored and uploaded while ensuring complete event information is captured.
Solution Approach 2:
The system extracts and separates event-related data from continuous motion capture streams by detecting specific motion patterns and thresholds. Only the extracted event segments are retained for storage and analysis, while non-event portions are discarded, significantly reducing storage requirements.
2Measurement precision
If multiple sensors are integrated for robust event detection, then detection accuracy improves, but device complexity and power consumption increase
Solution Approach 1:
The system uses a single motion capture sensor that performs multiple functions: detecting linear acceleration, detecting rotational acceleration, identifying events, and triggering video capture. This multi-functional approach achieves robust event detection without requiring separate specialized sensors for each measurement type.
Solution Approach 2:
The system combines linear acceleration data and rotational acceleration data from the motion capture sensor to detect events. By merging these measurement types within a single sensor platform, the system achieves comprehensive event detection capability while avoiding the complexity of integrating and synchronizing multiple separate sensors.
3Loss of information
If continuous motion data is uploaded for analysis, then comprehensive event analysis is possible, but bandwidth consumption and processing time increase
Solution Approach 1:
The system performs preliminary event detection and video trimming locally on the mobile device before upload. By pre-processing the data to identify and extract only event-containing segments, the system reduces the amount of data that needs to be uploaded and processed on remote servers, significantly decreasing processing time while maintaining analysis completeness.
4Quantity of substance
If event videos are trimmed to only include event portions, then storage requirements decrease, but risk of removing pertinent information increases
Solution Approach 1:
The system performs preliminary detection of event boundaries using motion data analysis before trimming video clips. By accurately identifying the start and end points of events through sensor data thresholds and patterns, the system ensures that all pertinent event information is retained while removing only non-event portions, thus reducing storage without compromising information completeness.
Data Source
AI summary
A sensor event detection system including a motion capture element with a memory, sensor, microprocessor, first communication interface and another sensor. The sensor captures values associated with an orientation, position, velocity and acceleration of the motion capture element. The first communication interface receives other values associated with a temperature, humidity, wind and elevation, and the other sensor locally captures the other values. The microprocessor collects data that includes sensor values from the sensor, stores the data in the memory, and recognizes an event within the data to determine event data. The microprocessor correlates the data or event data with the other values to determine a false positive event or a type of equipment the motion capture element is coupled with, or a type of activity indicated by the data or event data, and transmits the data or event data associated with the event via the first communication interface.