Sensor Group Linking Across Time Intervals for Occupancy Detection
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Solution Overview
Problem
Current motion tracking systems for environmental control in structures lack efficient methods to link sensor data across time intervals and validate group formations, leading to inaccuracies in occupancy detection and energy management.
Innovation Solution
A system comprising multiple sensors and a controller that identifies groups of neighboring sensors sensing motion greater than a threshold, linking these groups across time intervals to track motion while accounting for distance, obstructions, and sensor spacing, ensuring valid group formations and energy-efficient control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is tracked across multiple time intervals to improve motion tracking accuracy, then occupancy detection accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments sensor data processing by dividing the sensor array into groups and processing each group's data independently across time intervals. This allows complex multi-interval tracking to be broken down into manageable segments, improving accuracy while controlling computational complexity through localized group processing rather than global data analysis
Solution Approach 2:
The system performs preliminary grouping of sensors into spatial clusters before conducting time-interval analysis. By pre-organizing sensors into groups based on their spatial relationships and characteristics, the system prepares data structures in advance that facilitate efficient multi-interval tracking without requiring complex real-time computations
2Reliability
If groups of neighboring sensors are identified and linked across time intervals to track motion accurately, then motion tracking reliability is improved, but processing time and computational load increase
Solution Approach 1:
The system merges data from multiple neighboring sensors into unified group representations that can be tracked across time intervals. By combining sensor readings within spatial groups and treating them as single trackable entities, the system achieves reliable motion tracking through aggregated data while reducing the number of individual processing operations required
Solution Approach 2:
The system implements periodic sampling of sensor groups at defined time intervals rather than continuous monitoring. This periodic approach maintains reliable motion detection by capturing motion events at regular intervals while significantly reducing computational load compared to continuous processing of all sensor data
3Measurement precision
If sensor groups are validated for proper formation considering distance and obstructions, then occupancy detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies validation rules with local quality by considering spatial relationships and obstruction characteristics specific to each sensor group's location and configuration. Rather than applying uniform validation criteria globally, the system adapts validation parameters based on local environmental factors, improving detection accuracy while managing complexity through localized rule application
4Productivity
If multiple sensors are coordinated to track motion across time intervals, then energy management efficiency is improved, but system complexity increases
Solution Approach 1:
The sensor groups serve multiple functions simultaneously: they detect occupancy, track motion patterns, validate spatial formations, and provide data for energy management decisions. This multi-functionality allows the system to achieve improved energy management efficiency through coordinated sensor operation without proportionally increasing complexity, as the same sensor groups perform diverse tasks
Data Source
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AI summary
Apparatuses, methods, apparatuses and systems for tracking motion are disclosed. One method includes identifying a group of sensors that includes a plurality of neighboring sensors sensing motion greater than a threshold during a time interval, and tracking motion, comprising linking the group to at least one past group of at least one past time interval.