Multi-Sensor Room Occupancy Detection to Reduce False Positives
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Solution Overview
Problem
Conventional occupancy detection systems in rooms, such as those using single sensors or timers, often result in high false positive and false negative rates, leading to inefficient energy usage and maintenance challenges.
Innovation Solution
A multi-sensor system with a control unit that uses neural network models to process sensor readings from various occupancy parameters, such as motion, temperature, and humidity, to accurately determine the occupancy status of a room and trigger appropriate control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single sensor-based systems are used to detect occupancy, then the device complexity is reduced, but the measurement precision and reliability deteriorate due to high false positive and false negative rates
Solution Approach 1:
The patent combines multiple sensors (motion sensors, proximity sensors, temperature sensors, humidity sensors, CO2 sensors) into a unified occupancy detection system. This merging of sensors allows the system to cross-validate readings and reduce false positives/negatives, thereby improving measurement precision while maintaining manageable device complexity through integrated processing.
Solution Approach 2:
The control unit serves multiple functions: it processes readings from various sensor types, determines occupancy status, calculates confidence values, and triggers appropriate control actions. This multi-functionality consolidates what could be separate systems into a single unified device, improving accuracy without proportionally increasing complexity.
2Device complexity
If timer-based systems are used to detect occupancy, then the device complexity is reduced, but the loss of time and productivity deteriorate due to extended occupancy classification
Solution Approach 1:
The system continuously monitors sensor readings and provides real-time feedback to the control unit, which adjusts occupancy status determination dynamically. This feedback mechanism eliminates the need for fixed timer delays, allowing the system to quickly and accurately determine when a room is truly unoccupied, reducing time loss without increasing complexity.
Solution Approach 2:
The occupancy detection system transitions from static timer-based classification to dynamic sensor-based classification that adapts to actual conditions. The system continuously evaluates multiple sensor inputs and adjusts its determination in real-time, enabling faster and more accurate occupancy status changes without requiring complex manual configuration.
3Device complexity
If motion sensors are used to detect occupancy, then the device complexity is reduced, but the reliability deteriorates due to high false negative rate from blind spots
Solution Approach 1:
The detection system is segmented into multiple sensors positioned at different locations within the room, each covering specific zones. Motion sensors, proximity sensors, and environmental sensors are distributed throughout the space, ensuring that no area is a blind spot. This segmentation improves reliability by ensuring comprehensive coverage while keeping individual sensor units simple.
Solution Approach 2:
The system uses a composite approach by integrating multiple types of sensors (motion, proximity, temperature, humidity, CO2) that detect different physical phenomena. This composite sensor array compensates for the limitations of individual sensor types, improving overall reliability through diversified detection methods without requiring any single complex sensor.
4Device complexity
If proximity sensors are used to detect occupancy, then the device complexity is reduced, but the reliability deteriorates due to high false positive rate leading to increased energy usage
Solution Approach 1:
The system merges proximity sensor readings with motion sensor readings, temperature sensor readings, humidity sensor readings, and CO2 sensor readings. This combination allows the system to distinguish between genuine occupancy (multiple sensor confirmations) and false positives (single sensor anomalies), improving reliability while reducing unnecessary appliance operation and energy waste.
Solution Approach 2:
The control unit continuously processes feedback from multiple sensors and adjusts occupancy determination based on consistent patterns across all sensor types. This feedback mechanism filters out false positives from proximity sensors by requiring corroboration from other sensors, thereby improving reliability and preventing unnecessary energy consumption from appliances in unoccupied rooms.
Data Source
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AI summary
A method for detecting an occupancy of a room (120,120a,120b,120n) comprises receiving, from a plurality of sensors (122a, 122b, 122n) located within the room, sensor readings indicative of corresponding occupancy parameters. The plurality of sensors (122a,122b,122n) are configured to measure the corresponding occupancy parameters. The method further comprises determining, based on the received sensor readings, one or more of an occupancy status of the room (120,120a,120b,120n), and a confidence value associated with the determined occupancy status. The occupancy status is indicative of one of a positive status indicating occupancy of the room and a negative status indicating non-occupancy of the room. The method further comprises, in response to determining the occupancy status, triggering a control action associated with the room (120,120a,120b,120n) based on the determined occupancy status.