Systems and methods for detecting occupancy of rooms
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Solution Overview
Problem
Conventional occupancy detection systems in rooms, such as those using single sensors or timers, often result in false positives and negatives, leading to inefficient energy usage due to blind spots and unbalanced detection, and are not resilient to sensor failures.
Innovation Solution
A system comprising multiple sensors and a control unit with processors that use neural network models to determine occupancy status and confidence values based on sensor readings, preprocessing data to generate preprocessed sequences, and trigger control actions for HVAC and lighting systems.
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 blind spots and sensor failures
Solution Approach 1:
The detection system is segmented into multiple independent sensor units distributed throughout the room, each covering specific zones. This segmentation eliminates blind spots while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
Multiple sensor readings from different locations and types are merged and processed together to form a comprehensive occupancy determination. The control unit integrates data from all sensors to achieve high-precision detection that surpasses individual sensor capabilities.
2Device complexity
If motion sensors are used to detect occupancy, then the device complexity remains low, but the measurement precision deteriorates due to high false negative rates
Solution Approach 1:
Motion sensor data is combined with readings from other sensor types (proximity, occupancy sensors) to compensate for the high false negative rate of motion sensors alone, achieving more reliable occupancy detection without significantly increasing system complexity.
Solution Approach 2:
The system uses feedback from multiple sensor sources to validate and correct motion sensor readings, reducing false negatives by cross-checking occupancy status against data from other sensors in the network.
3Device complexity
If proximity sensors are used to detect occupancy, then the device complexity remains low, but the measurement precision deteriorates due to high false positive rates
Solution Approach 1:
Proximity sensor readings are subjected to feedback validation through comparison with data from other sensors and temporal analysis of reading sequences, filtering out false positives while maintaining the simplicity of proximity sensing.
Solution Approach 2:
The system dynamically evaluates proximity sensor readings by analyzing sequences of measurements over time and adjusting detection thresholds based on contextual information from other sensors, reducing false positives without requiring additional hardware.
4Device complexity
If timer-based systems are used to classify occupancy, then the device complexity is minimized, but the measurement precision deteriorates due to prolonged occupied classification
Solution Approach 1:
Timer-based classification is enhanced with feedback from actual sensor readings that continuously validate or correct the occupancy status, preventing prolonged misclassification while maintaining the simplicity of timer-based logic.
Solution Approach 2:
The system employs periodic sensor readings and status re-evaluations at defined intervals, combining timer-based classification with periodic verification to ensure accurate occupancy status transitions without requiring continuous complex processing.
Data Source
AI summary
Embodiments of the disclosure describe systems and methods for detecting an occupancy of a room. The method comprises receiving, from a plurality of sensors located within the room, sensor readings indicative of corresponding occupancy parameters. The plurality of sensors 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, 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 based on the determined occupancy status.