Sound-Based Occupancy Tracking for Personalized HVAC Control
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Solution Overview
Problem
Existing HVAC systems cannot determine the number of people present in a space or identify individuals, limiting their ability to provide personalized temperature settings and efficient energy management.
Innovation Solution
An occupancy tracking system that uses sound recognition, user device detection, and wireless signal distortion analysis to predict the number of people in a space, allowing for personalized HVAC control based on individual preferences and occupancy levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proximity sensors or motion detection sensors are used to determine occupancy, then the system can detect whether a space is occupied, but it cannot determine the number of people present or identify individuals
Solution Approach 1:
The patent replaces mechanical sensors (proximity sensors, motion detection sensors) with acoustic field-based detection (microphones capturing voice signals). This substitution enables the system to extract richer information from the acoustic environment, including voice presence, voice count, and voice characteristics, thereby determining both the number of people and their identities without using mechanical sensing methods.
Solution Approach 2:
The patent changes the detection parameter from binary occupancy status (occupied/not occupied) to multiple acoustic parameters including voice presence, voice count, and voice characteristics. By analyzing these transformed parameters from the acoustic field, the system can determine the number of people and their identities, resolving the information loss problem while maintaining detection accuracy.
2Ease of operation
If existing HVAC systems use binary occupancy detection, then the system operation is simple, but it cannot provide personalized HVAC settings for different individuals
Solution Approach 1:
The patent introduces dynamic adaptability into the HVAC system by enabling it to identify different individuals through voice characteristics and adjust settings in real-time based on who is present. The system dynamically changes HVAC parameters (temperature, ventilation) according to the detected occupants' preferences, transforming a static binary control system into a dynamic personalized control system.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors voice signals, identifies occupants, determines their preferred HVAC settings, and adjusts the environment accordingly. This closed-loop feedback enables personalized control while maintaining operational simplicity, as the system automatically adapts without requiring manual user input.
3Measurement precision
If the system captures audio signals to determine occupancy, then it can identify the number of people, but it may incorrectly include voices from electronic devices
Solution Approach 1:
The patent applies inversion by analyzing what voices are NOT present rather than just detecting voice presence. By examining the absence or presence of expected voice signals from known occupants, the system can distinguish between human voices and electronic device sounds, effectively filtering out false positives from TVs, radios, and other electronic devices.
Solution Approach 2:
The patent introduces voiceprint analysis as an intermediary layer between raw audio capture and occupancy determination. This intermediary process analyzes voice characteristics and patterns to differentiate between human voices and electronic device sounds, acting as a filter that eliminates false positives while preserving accurate occupancy counting.
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
Enables more accurate prediction of occupancy levels and personalized HVAC management, improving energy efficiency and comfort by adjusting settings according to the number and preferences of individuals in the space.
Implementation Method 1
an occupancy tracking system that uses sound recognition, user device detection, and wireless signal distortion analysis to predict the number of people in a space
Implementation Method 2
wireless signal distortion analysis to predict the number of people in a space
Data Source
AI summary
An occupancy tracking device configured to receive a plurality of sound samples over a predetermine time period. The device is further configured to compute an audio signature for each sound sample. The audio signature includes a numerical value that uniquely identifies characteristics of an audio signal. The device is further configured to determine a direction of arrival for each sound sample. The device is further configured to populate entries in the voice data log for the sound samples, to identify one or more clusters based on an audio signature that is associated with the populated entries, and to determine a number of clusters that are identified. The device is further configured to determine a predicted occupancy level based on the number of clusters that are identified and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.