Dense Sensor Field Object Tracking for Reliable Event Detection
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Solution Overview
Problem
Existing object tracking systems face challenges with inaccurate and unreliable information due to instantaneous or short-duration sensor observations, require extensive processing power, and are inflexible when changes occur in monitored spaces, leading to high modification costs and operational errors.
Innovation Solution
A dense sensor field system comprising multiple touch and pressure sensors with advanced data processing capabilities that track objects by analyzing sequential sensor observations over time, using predefined event conditions to produce accurate and reliable event information, and adapting to changes in monitored spaces without requiring extensive modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If instantaneous or short-duration sensor observations are used for object detection, then the response time is fast, but the reliability and accuracy of event information deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing sensor observations in a data structure before an event occurs. When an event is detected, the system already has accumulated observation data ready for immediate analysis, thus maintaining fast response time while ensuring reliable event information through pre-gathered evidence.
Solution Approach 2:
The system maintains continuous sensor observation and data collection throughout the monitored space, ensuring that useful action (data gathering) never stops. This continuity provides both the speed of immediate detection and the reliability of accumulated evidence, as observations are constantly being updated and stored for event verification.
2Measurement precision
If camera surveillance with automated image interpretation is used, then the accuracy of event detection is improved, but the processing power requirement and equipment cost increases
Solution Approach 1:
The system extracts only the essential features needed for event detection from sensor observations, rather than processing complete images. By taking out only the relevant data elements (presence, location, movement characteristics), the system achieves accurate event detection with minimal processing power requirements.
Solution Approach 2:
The system uses simple, inexpensive sensor observations that are processed and discarded after use, rather than relying on expensive camera systems. Each sensor observation is a low-cost data point that contributes to event detection but requires minimal processing, effectively replacing costly imaging equipment with economical sensing.
3Measurement precision
If detectors are installed in fixed locations to monitor specific events, then the detection accuracy for those events is improved, but the adaptability to changes in space usage deteriorates
Solution Approach 1:
The sensor field provides universal coverage of the entire monitored space, with each sensor capable of detecting events in its local area. This multi-functional sensor network can adapt to any event type or location by processing observations from different sensors, eliminating the need for fixed detector placements and enabling flexible response to changing space usage.
Solution Approach 2:
The system dynamically adapts to changes in space usage by processing sensor observations in real-time and adjusting event detection based on current conditions. Rather than having static detector configurations, the system's data processing apparatus continuously evaluates observations from across the sensor field, allowing it to respond flexibly to moving events or changing monitoring requirements.
4Reliability
If a dense sensor field is used for object tracking, then the reliability of event information is improved, but the processing power requirement increases
Solution Approach 1:
The system segments the monitored space into multiple zones covered by different sensors in the dense sensor field. Each sensor independently processes observations in its local area, and the data processing apparatus combines these segmented results to form complete event information. This segmentation allows reliable event detection through multiple observations while distributing processing requirements across multiple simple sensor units rather than requiring one complex central processor.
Data Source
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AI summary
The system according to the invention interprets sensor observations by tracking objects and by collecting information about the objects by means of the tracking and by using this information for affirming events linked to the objects and in producing information describing the events. The system detects events according the conditions defined for them, on the basis of sensor observations. The conditions can relate to the essence of the objects, e.g. to the strength of the observations linked to the object, to the size and/or shape of the object, to a temporal change of essence and to movement. The event conditions used by the system can comprise conditions applying to the location of the object. The system according to the invention can be used e.g. for detecting the falling, the getting out of bed, the arrival in a space or the exit from it of a person by tracking an object with a dense sensor field, and for producing event information about the treatment or safety of the person for delivering to the person providing care.