Adaptive Threshold Manipulation for PIR Sensor Noise Compensation
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Solution Overview
Problem
Occupancy sensors in smart devices often produce noise, leading to varying detection thresholds, resulting in inconsistent human detection and false positives due to differences in sensor sensitivity and environmental conditions, which reduces detection sensitivity and accuracy.
Innovation Solution
Adaptive threshold adjustment based on the sensitivity characteristics of individual sensors, recalculated by a high-power processor using measurements from unoccupied periods to improve human detection accuracy and distinguish between humans and other objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a high occupancy threshold is used to avoid false positives from noise, then reliability is improved, but occupancy detection sensitivity deteriorates
Solution Approach 1:
The patent applies local quality by customizing the occupancy threshold for each individual sensor based on its specific noise characteristics. Instead of using a uniform high threshold for all sensors, the system measures the actual noise level of each sensor and sets a tailored threshold that is locally optimized for that sensor's performance, thereby maintaining reliability while improving detection sensitivity for each specific sensor instance
Solution Approach 2:
The patent changes the threshold parameter dynamically based on measured noise characteristics. The system measures the noise level of each sensor during manufacturing or calibration and uses this measurement to determine an appropriate threshold value. This parameter change allows the system to adapt the threshold to match actual sensor performance rather than relying on conservative fixed values
2Device complexity
If a fixed occupancy threshold is used across all devices, then device complexity is reduced, but detection consistency deteriorates
Solution Approach 1:
The patent applies preliminary action by measuring and characterizing each sensor's noise properties during manufacturing or initial setup, before the sensor is deployed in the field. This preliminary measurement allows the system to pre-determine appropriate threshold values for each sensor, ensuring consistent detection performance across all devices without requiring complex runtime adjustments or calibration procedures
Solution Approach 2:
The system performs self-service by automatically measuring the noise characteristics of each sensor and determining its own optimal threshold value without requiring manual calibration or external intervention. Each sensor essentially calibrates itself during manufacturing or initial operation, eliminating the need for complex external configuration while ensuring detection consistency
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
Enhances the consistency and accuracy of human detection in smart devices by compensating for sensor-specific noise levels, reducing false positives and improving sensitivity, allowing for earlier detection of human presence.
Implementation Method 1
one or more of the electronic devices may use an occupancy sensor (e.g., a passive infrared (PIR) sensor) to sense the presence of the human
Data Source
AI summary
A method for adaptively adjusting a threshold used to detect the presence of a living being may include receiving a first set of sensor measurements acquired by a passive infrared (PIR) sensor during a time period when the living being is not expected to be present in a space monitored by the PIR sensor. Here, the sensor measurements may depend on one or more noise sensitivity characteristics of the PIR sensor. The method may include adjusting a threshold that may indicate a presence of the living being based on the first set of sensor measurements. The method may then receive a second set of sensor measurements acquired by the PIR sensor and detect the presence of the living being when at least one of the second set of sensor measurements exceeds the threshold.