Occupancy Sensing With Adaptive Thresholds and Motion Analysis
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Solution Overview
Problem
Current occupancy sensing technologies, such as passive infrared sensors, are inadequate for accurately determining occupancy in complex automated workspaces, often leading to incorrect activation or deactivation of systems due to their sensitivity to motion and presence detection.
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 algorithms to differentiate between human and non-human movements, and incorporates multiple sensors for enhanced accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional passive infrared sensors are used for occupancy detection, then the system is simple and low cost, but the occupancy detection accuracy is insufficient leading to false triggers
Solution Approach 1:
The patent segments the occupancy detection task across multiple sensor types (passive infrared sensors, motion sensors, and occupancy sensors) rather than relying on a single sensor. Each sensor type detects different aspects of occupancy (thermal signatures, motion patterns, presence), and their combined output provides more accurate detection while reducing false positives that would occur with any single sensor type alone.
2Reliability
If the motion sensor threshold is set low to detect all movements, then sensitivity is high, but false triggers increase due to non-human movements
Solution Approach 1:
The system uses feedback loops where sensor outputs are continuously monitored and fed back into the processing algorithm. The neural network or machine learning model analyzes patterns in the feedback data over time, learning to distinguish between human-generated motion patterns and non-human disturbances. This adaptive feedback mechanism allows the system to maintain high sensitivity while reducing false triggers through pattern recognition.
Solution Approach 2:
The patent dynamically adjusts detection parameters based on environmental conditions and learned patterns. Rather than using fixed thresholds, the system modifies sensitivity parameters, time windows, and detection criteria based on historical data and current context, allowing it to maintain high reliability across varying conditions while minimizing false triggers from non-human sources.
3Measurement precision
If multiple sensors are deployed to improve detection accuracy, then occupancy detection reliability improves, but system cost and complexity increase
Solution Approach 1:
The patent designs the sensor system so that each sensor type serves multiple functions. Passive infrared sensors not only detect occupancy but also provide thermal pattern data for authentication. Motion sensors detect both presence and movement characteristics for activity recognition. This multi-functionality reduces the need for additional specialized sensors, thereby controlling costs while maintaining high detection accuracy through diverse data inputs.
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 the reliability of occupancy-based automation systems, while maintaining low operational costs and energy efficiency.
Implementation Method 1
a motion sensor that generates a motion signal in response to a sensed motion in a space
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.