Occupancy Sensing with Adaptive PIR Thresholds and Motion Analysis
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Solution Overview
Problem
Traditional occupancy sensing systems, such as PIR sensors, are not accurate enough for advanced workspaces that require precise occupancy determination for automated processes, often leading to incorrect lighting control and inefficient resource management.
Innovation Solution
A motion sensor system that operates in a high threshold mode initially and switches to a low threshold mode upon detecting motion, using a time-dependent signal threshold and human-like motion analysis to differentiate between human and non-human movements, and includes features like fall detection and multi-sensor coordination to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PIR sensors are used for occupancy sensing, then the system is simple and low cost, but the occupancy detection accuracy is insufficient leading to false triggers
Solution Approach 1:
The patent combines multiple motion sensors (including PIR sensors and other types) into a sensor array that works together to detect occupancy. By merging multiple sensing elements, the system achieves higher detection accuracy through signal correlation and pattern recognition, while managing complexity through coordinated operation of the sensor array.
Solution Approach 2:
The patent implements dynamic threshold adjustment where the signal threshold changes based on the operating mode. The system operates in high threshold mode during normal conditions and switches to low threshold mode when motion is detected, allowing adaptive optimization of detection sensitivity rather than using a fixed threshold.
2Reliability
If a low threshold is used continuously for accurate motion detection, then occupancy detection sensitivity is high, but false triggers from non-human motion increase
Solution Approach 1:
The system dynamically adjusts the signal threshold based on the operating mode. During normal operation, a high threshold is used to filter out false triggers from non-human motion. When motion exceeding the high threshold is detected, the system switches to a low threshold mode to ensure accurate occupancy detection, thus adapting the threshold to current conditions rather than using a static value.
Solution Approach 2:
The system uses feedback from motion signal analysis to determine when to switch between operating modes. By continuously monitoring motion signals and comparing them against thresholds, the system provides feedback that triggers mode transitions, ensuring reliable detection while minimizing false alarms through adaptive threshold selection.
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 system provides more accurate occupancy detection, reducing false triggers and improving resource management in complex workspaces by adapting to different motion patterns and environments.
Implementation Method 1
a passive infrared (PIR) sensor that controls room lights
Data Source
AI summary
The present disclosure provides systems and methods for improved occupancy sensing. The methods and systems can deploy various signal threshold adjustments and/or signal analysis algorithms in response to sensed signals having a given quality, such as exceeding a threshold. In some cases, signal thresholds are lowered following an initial generated signal exceeding a first, higher threshold. In some cases, time-dependent signals are monitored using algorithms that analyze the signals for variations that are characteristic of human usage. Methods are disclosed for determining if two motion sensors are observing the same or overlapping spaces. Systems and methods for calibrating motion sensing systems are also disclosed.