Wi-Fi Activity Detection Using CWT for Privacy-Safe Sensing
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Solution Overview
Problem
Existing Wi-Fi based motion detection techniques are limited by the need for specialized hardware setups, specific channel configurations, and machine learning models, which restrict their applicability and accuracy, especially in mesh networks, and often suffer from low detection and classification accuracy.
Innovation Solution
The use of Continuous Wavelet Transformation (CWT) to analyze Wi-Fi signal reflections, combined with machine learning algorithms, allows for accurate detection and classification of activities in real-time, integrating with existing Wi-Fi infrastructure and addressing privacy concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional activity monitoring methods like video surveillance are used, then detection accuracy is improved, but privacy concerns arise and intrusiveness increases
Solution Approach 1:
The patent replaces video surveillance (optical/mechanical system) with Wi-Fi signal-based detection (electromagnetic field system). Instead of using cameras to visually monitor activities, the system uses Wi-Fi signals to detect changes in the environment caused by human movement, thereby achieving accurate detection without the privacy intrusion of visual surveillance.
2Measurement precision
If specialized hardware setups and specific channel configurations are used for Wi-Fi detection, then detection accuracy is improved, but device complexity and applicability restrictions increase
Solution Approach 1:
The patent makes Wi-Fi detection work with standard existing Wi-Fi infrastructure rather than requiring specialized hardware. By using commercially available Wi-Fi devices and standard channels, the system achieves broad applicability across different environments without needing custom-built specialized equipment, thus reducing device complexity while maintaining detection capabilities.
3Ease of operation
If existing Wi-Fi based detection techniques are used, then implementation is simpler, but detection accuracy and classification accuracy decrease
Solution Approach 1:
The patent improves detection accuracy by changing the parameters used in Wi-Fi signal analysis. Instead of using basic signal strength measurements, the system analyzes phase information, amplitude information, and temporal patterns of Wi-Fi signals. This parameter transformation allows standard Wi-Fi infrastructure to achieve high detection and classification accuracy without requiring specialized hardware.
4Area of stationary object
If mesh networks are used for Wi-Fi detection, then coverage area is improved, but applicability and accuracy are restricted
Solution Approach 1:
The patent enables Wi-Fi detection to function effectively in mesh networks by using standard Wi-Fi communication protocols and channels that are already universally supported. The system can operate with multiple access points and routers in a mesh configuration, leveraging the existing multi-node architecture to provide both wide coverage and accurate detection without requiring specialized mesh network hardware or configurations.
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
CWT provides high accuracy (98-99%) for activity detection, operates in real-time, and adapts to environments, enhancing detection accuracy while being non-intrusive and cost-effective for various applications.
Implementation Method 1
a modified wireless signal based on the emitted wireless signal modified by the object in the environment
Implementation Method 2
a modified wireless signal based on the emitted wireless signal modified by the object in the environment
Data Source
AI summary
A method may include receiving a wireless signal from the environment by a receiver. The method may include determining, at least one of phase information or amplitude information associated with the wireless signal. The method may include generating, using a continuous wavelet transformation, at least one of phase data or amplitude data based on the phase information or the amplitude information, respectively. The method may include providing at least one of the phase data or the amplitude data to an image generation module executed by the receiver. The method may include processing, by the image generation module executed by the receiver, at least one of the phase data or the amplitude data to generate an image of the environment. The method may include detecting, by the receiver and based on the image, the object within the environment.