Wireless Event Recognition Using Multipath Channel Classification
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Solution Overview
Problem
Conventional security systems are costly, difficult to install and upgrade, prone to false alarms, and lack accuracy in detecting object motion, especially in environments with environmental changes or solid barriers.
Innovation Solution
A wireless monitoring system using time-reversal technology to detect periodic motions like human breathing by transmitting and receiving wireless signals through a multipath channel, processing channel information to classify events and recognize known or unknown occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security systems use multiple contact sensors per door or window, then detection coverage is improved, but hardware cost and installation complexity increase significantly
Solution Approach 1:
The wireless monitoring device performs multiple functions including motion detection, periodic motion detection, event recognition, and classification of various events (door opening, window opening, intruder detection, breathing detection) using a single integrated system, eliminating the need for multiple separate sensors and reducing installation complexity
Solution Approach 2:
The system replaces physical contact sensors and wiring infrastructure with wireless monitoring technology that uses radio frequency signals to detect motion and periodic motion, eliminating the need for complex electrical wiring and mechanical sensor installation
2Difficulty of detecting and measuring
If conventional motion detection systems use PIR sensors, then human motion detection is achieved, but false alarms increase due to environmental sensitivity
Solution Approach 1:
The system detects periodic motions such as breathing patterns by analyzing rhythmic movements over time, distinguishing between intentional periodic actions (breathing) and random environmental fluctuations, thereby reducing false alarms while maintaining detection sensitivity
Solution Approach 2:
The system uses machine learning classifiers that continuously learn from training data to distinguish between genuine events and false alarm conditions, improving detection accuracy by providing feedback-based classification that adapts to environmental variations
3Area of stationary object
If ultrasonic sensors are used for motion detection, then detection range is extended, but detection accuracy decreases due to inability to penetrate solid objects
Solution Approach 1:
The system dynamically adapts its detection methodology by using wireless signal propagation characteristics that can penetrate or diffract around solid objects, maintaining detection accuracy in environments with furniture and barriers where ultrasonic sensors fail
4Stability of the object's composition
If conventional security systems are installed with wiring, then system stability is improved, but upgradeability and maintenance become difficult
Solution Approach 1:
The system replaces wired mechanical connections with wireless communication technology, allowing the monitoring devices to communicate data and receive updates without physical connections, thereby enabling easy upgrades and maintenance while maintaining system stability through wireless protocol 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
The system provides high accuracy in detecting security-related events and motions with low hardware costs, easy installation, and the ability to monitor large areas with minimal devices, overcoming limitations of traditional systems.
Implementation Method 1
transmitting, by an antenna of a first transmitter, a respective training wireless signal to at least one first receiver through a wireless multipath channel impacted by the known event
Implementation Method 2
wireless multipath channel impacted by the known event in the venue
Data Source
AI summary
Apparatus, systems and methods for recognizing and classifying events in a venue based on a wireless signal are disclosed. In one example, a disclosed system comprises a first transmitter, a second transmitter, at least one first receiver, at least one second receiver, and an event recognition engine, in the venue. The first transmitter transmits a training wireless signal through a wireless multipath channel impacted by a known event in the venue in a training time period associated with the known event. Each first receiver receives asynchronously the training wireless signal, and obtains, asynchronously based on the training wireless signal, at least one time series of training channel information of the wireless multipath channel between the first receiver and the first transmitter. The second transmitter transmits a current wireless signal through the wireless multipath channel impacted by a current event in a current time period associated with the current event. Each second receiver receives asynchronously the current wireless signal, and obtains, asynchronously based on the current wireless signal, at least one time series of current channel information of the wireless multipath channel between the second receiver and the second transmitter. The event recognition engine trains a classifier based on the training channel information; and apples the classifier to: classify the current channel information and associate the current event with at least one of: a known event, an unknown event and another event.