Occupancy Estimation Using Binary PIR Sensor Sequences
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Solution Overview
Problem
Existing systems for estimating the number of occupants in a room using passive infrared sensors face challenges due to the binary output nature of PIR sensors, which are costly and less reliable with high false positive and true negative results, and require complex modifications for analog signal-based solutions.
Innovation Solution
A system that integrates a PIR sensor outputting a binary signal sequence with a computing unit using machine learning to estimate the number of persons, potentially aided by multiple sensors and additional environmental sensors like air pollution, sound level, and temperature sensors, to accurately count occupants with minimal modifications to existing setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If PIR sensors are used as binary sensors for presence detection, then the system is simple and cost-effective, but the measurement precision for counting occupants is insufficient due to high false positive and true negative results
Solution Approach 1:
The system transitions from static binary presence detection to dynamic sequence analysis by capturing temporal patterns of PIR sensor outputs. The machine learning model analyzes sequences of binary signals over time to distinguish between different occupancy scenarios, enabling accurate counting while maintaining binary sensor simplicity.
Solution Approach 2:
The invention changes the parameter being analyzed from simple presence/absence binary states to temporal sequence patterns. By examining the time-dependent behavior and transitions of binary PIR signals, the system extracts additional information that enables accurate occupancy estimation without requiring analog sensors or complex hardware modifications.
2Measurement precision
If analog signal-based PIR sensors are used for occupancy estimation, then the measurement precision improves, but the device complexity and cost increase due to required sensor modifications
Solution Approach 1:
Instead of modifying the physical PIR sensor to output analog signals, the system creates a virtual analog representation through machine learning processing of binary signals. The trained model effectively copies the functionality of analog-based systems by learning to interpret temporal patterns in binary outputs, achieving similar accuracy without hardware changes.
Solution Approach 2:
The invention replaces the need for analog signal processing hardware with a software-based machine learning approach. Instead of modifying the PIR sensor's mechanical/electrical output characteristics, the system substitutes binary signal processing with intelligent algorithms that extract occupancy information from temporal patterns.
3Measurement precision
If multiple PIR sensors are deployed to improve occupancy counting, then the measurement precision increases, but the device complexity and cost increase
Solution Approach 1:
The system segments the occupancy detection task across multiple binary PIR sensors, with each sensor monitoring a specific zone. The machine learning model integrates sequences from multiple sensors to estimate total occupancy, leveraging spatial distribution to improve accuracy while maintaining binary sensor simplicity and reducing overall system cost.
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
Enables accurate and cost-effective estimation of the number of occupants in a room using existing binary PIR sensors, improving reliability and reducing effort, with the ability to integrate with existing building systems and provide real-time occupancy data for optimized room management.
Implementation Method 1
A passive infrared sensor (PIR sensor) detects changes in the amount of infrared radiation
Data Source
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AI summary
The present invention relates to a system for estimating the number of persons (P) inside an area (1), wherein the system comprises at least one passive infrared sensor (S1..S3) configured to detect the presence of an object (P) in at least a part (A1..A3) of the area (1) and to output a binary signal sequence depending on presence of at least one person (P) in the part (A1..A3) of the area (1), and a computing unit (13) configured to estimate the number of persons (P) inside the area (1) based on the binary signal sequence by machine learning.