PIR Sensor Framework for Human Motion Detection
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Solution Overview
Problem
Conventional passive infrared (PIR) sensors often generate false alerts due to noise from external environmental radiation sources, such as heaters, which can mimic human motion, leading to inaccurate detection and reduced signal-to-noise ratio (SNR) in devices with limited size, battery power, and computational capacity.
Innovation Solution
A data-driven statistical framework that extracts local and global features from PIR sensor signals to differentiate between human motion and environmental radiation sources, using a scoring function to accurately determine human presence while minimizing computational complexity and battery consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional PIR sensors are used to detect human motion, then motion detection capability is provided, but false detection rate increases due to environmental radiation sources
Solution Approach 1:
The patent segments the detection process into multiple independent components: local feature extraction (analyzing signal patterns), global feature extraction (analyzing environmental context), and a scoring function that combines both. This segmentation allows the system to separately evaluate human motion indicators versus environmental radiation indicators, resolving the contradiction by enabling accurate differentiation between true targets and harmful interference.
Solution Approach 2:
The patent introduces a scoring function as an intermediary between raw PIR sensor data and detection decisions. This scoring function integrates local and global features to produce a comprehensive evaluation metric, acting as a mediator that filters out false detections from environmental sources while preserving true human motion detections, thereby improving reliability without increasing false detection rate.
2Measurement precision
If advanced signal processing is implemented to reduce false detections, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the complex signal processing task into two independent feature extraction modules (local and global) that can be computed separately and then combined through a simple scoring function. This segmentation reduces computational complexity by avoiding the need for a single complex processing algorithm, while still achieving high detection precision through the complementary nature of the two feature types.
Solution Approach 2:
The patent extracts only the most relevant features from the PIR signal (local temporal patterns and global environmental context) rather than processing the entire signal spectrum. This partial action approach achieves sufficient detection precision without the computational burden of exhaustive signal analysis, resolving the contradiction between precision and complexity.
3Reliability
If continuous monitoring is performed to maintain detection accuracy, then detection reliability is maintained, but energy consumption increases
Solution Approach 1:
The patent implements periodic feature extraction and scoring at strategically chosen intervals rather than continuous processing. The system monitors the PIR signal and triggers detailed analysis only when relevant changes occur, maintaining detection reliability through periodic updates while significantly reducing average power consumption compared to continuous monitoring.
Solution Approach 2:
The patent performs partial signal processing by extracting only essential local and global features rather than conducting full signal analysis at all times. This selective processing maintains adequate detection reliability for battery-powered applications by processing only the minimum necessary data to sustain reliable operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The framework significantly reduces false detection rates and improves battery life by accurately distinguishing human motion from environmental noise, even in challenging heat scenarios, with low computational costs and robust human motion detection capabilities.
Implementation Method 1
Passive infrared (PIR) sensors can detect human motion by measuring infrared variations in a scene
Implementation Method 2
detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV)
Data Source
AI summary
A method includes detecting, with a passive infrared sensor (PIR), a level of infrared radiation in a field of view (FOV) of the PIR, generating a signal based on detected levels over a period of time, the signal having values that exhibit a change in the detected levels, extracting a local feature from a sample of the signal, wherein the local feature indicates a probability that a human in the FOV caused the change in the detected levels, extracting a global feature from the sample of the signal, wherein the global feature indicates a probability that an environmental radiation source caused the change in the detected levels, determining a score based on the local feature and the global feature, and determining that a human motion has been detected in the FOV based on the score.