Monitoring System Detecting Events via Sensor and Audio Data Rhythm
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Solution Overview
Problem
Traditional property monitoring systems typically evaluate sensor data in isolation, failing to detect complex events that involve patterns of activity over time or specific patterns of sound and energy usage, which limits their ability to identify expected or unexpected events accurately.
Innovation Solution
A monitoring system that incorporates sensors, microphones connected to pipes, and a monitoring control unit to analyze sensor and audio data, identifying patterns of activity to determine the usage of devices and perform appropriate actions based on learned rhythms and flows of a property.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is evaluated in isolation using traditional monitoring systems, then the system structure remains simple, but the ability to detect complex events and identify patterns of activity is insufficient
Solution Approach 1:
The patent combines multiple sensor data streams (motion sensors, contact sensors, glass-break sensors, temperature sensors, humidity sensors, smoke detectors, carbon monoxide detectors) with audio data from microphones into a unified monitoring system. This merging enables the system to detect complex events by analyzing patterns across multiple data sources simultaneously, thereby improving event detection accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The patent adds the dimension of audio data analysis to traditional sensor-based monitoring systems. By incorporating microphone data and analyzing acoustic patterns alongside sensor readings, the system creates a multi-dimensional approach to event detection. This additional dimension enables detection of events that would be invisible to sensor-only systems, such as audio-based leak detection or unusual appliance sounds.
2Reliability
If the system monitors only individual sensor triggers, then the response time is immediate, but the ability to identify expected versus unexpected events based on patterns is limited
Solution Approach 1:
The system performs preliminary learning of normal property rhythms and patterns during periods when no events are occurring. By establishing baseline patterns of sensor and audio data during normal operation, the system prepares reference models that enable faster real-time event evaluation. This preliminary action reduces the time needed for pattern analysis during actual monitoring, as the system already has established benchmarks for comparison.
Solution Approach 2:
The system continuously compares current sensor and audio data against learned normal patterns and provides feedback to refine its understanding of property-specific rhythms. This feedback mechanism improves event identification reliability over time by adapting to changes in property usage patterns while maintaining the ability to detect deviations that indicate events.
3Measurement precision
If the system uses multiple sensors and microphones to detect patterns, then the detection capability improves, but the data processing and analysis complexity increases
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: sensor data acquisition, audio data acquisition, pattern learning module, event detection module, and response generation module. Each module processes specific types of data and performs dedicated functions. This segmentation reduces processing complexity by dividing the overall task into manageable, specialized components that can operate independently and efficiently.
Solution Approach 2:
The system introduces an intermediary processing layer that aggregates and pre-processes data from multiple sensors and microphones before passing it to the pattern recognition algorithms. This intermediary layer normalizes data formats, filters noise, and identifies preliminary patterns, thereby reducing the complexity of subsequent analysis while maintaining high detection precision.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a storage device, for a monitoring system that is configured to monitor a property. The monitoring system may include a sensor that is configured to generate sensor data that reflects an attribute of the property, a microphone that is connected to a pipe and that is configured to detect audio data associated with the pipe, and a monitor control unit. The monitor control unit is configured to receive the sensor data and the audio data, provide the sensor data and the audio data as inputs to a model that is trained to determine that a device connected to the pipe is being used, receive, from the model, data indicating that the device connected to the pipe is being used, and based on determining that the device connected to the pipe is being used, perform a monitoring system action.