Classifying Static Leaf Nodes in Motion Detection Systems
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Solution Overview
Problem
Existing motion detection systems using wireless signals face challenges in accurately determining motion within a space due to the inclusion of both static and mobile leaf nodes, which can lead to poor channel information quality and system degradation, especially when a large number of leaf nodes appear simultaneously or during system initialization.
Innovation Solution
A closed-loop continuous link health measurement and classification system is implemented to differentiate between fixed and mobile leaf nodes by analyzing network status reports and classifying AP-leaf node links based on metrics such as presence information, successful sounding rates, and signal strength, allowing the system to select only static leaf nodes for motion detection, thereby improving data quality and system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If both static and mobile leaf nodes are included in motion detection, then the system can detect motion from more sources, but the accuracy of motion detection deteriorates due to poor channel information quality from mobile nodes
Solution Approach 1:
The patent extracts and removes mobile leaf nodes from the motion detection system by classifying leaf nodes based on their presence activity metrics. The system identifies mobile nodes through calibration periods and excludes them from providing channel information, thereby preventing degradation of motion detection accuracy while maintaining the benefits of having multiple static leaf nodes.
2Quantity of substance
If a large number of leaf nodes are included in the system, then more channel information is available, but system performance deteriorates due to overload and processing complexity
Solution Approach 1:
The patent extracts only the necessary subset of leaf nodes (static nodes with high presence activity) for motion detection, removing mobile nodes and low-quality static nodes from the active detection pool. This reduces the processing load on the system while maintaining sufficient channel information quality for accurate motion detection.
Solution Approach 2:
The patent applies different quality standards to different leaf nodes by classifying them based on their presence activity during calibration periods. Static leaf nodes with high presence activity are selected for motion detection, while mobile nodes and low-quality nodes are excluded. This localized quality control optimizes system performance by using only high-quality data sources.
3Adaptability or versatility
If mobile leaf nodes are included in motion detection, then the system is more adaptable to dynamic environments, but the reliability of channel information deteriorates
Solution Approach 1:
The patent extracts and removes mobile leaf nodes from the motion detection process by implementing a classification system that identifies nodes based on their presence activity during calibration periods. Mobile nodes are excluded from providing channel information, ensuring that only reliable data from static nodes is used for motion detection, thereby maintaining high channel information quality.
Data Source
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AI summary
In a general aspect, a motion detection system manages leaf nodes used for sounding by one or more access points. For example, an access point identifies one or more static leaf nodes based on presence activity of each leaf node in a calibration window. A health score is determined for each AP-leaf node link for each calibration window based on AP-leaf node link quality information. One or more of the static leaf nodes are selected to be used for sounding in the motion detection system based on the health scores for each of the AP-leaf node links. The motion detection system is then updated to use the selected one or more static leaf nodes for motion detection.