AI Activity Detection Device with Local Multi-Modal Sensor Fusion
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Solution Overview
Problem
Conventional monitoring devices for surveillance and tracking applications have low detection accuracy, high false alarm rates, and power consumption issues, failing to distinguish between similar objects and requiring centralized processing that delays real-time responses, while also raising privacy concerns.
Innovation Solution
A compact AI-enabled device with integrated multi-modal sensors and AI analyzers that captures and analyzes sound, image, and environmental data locally, reducing false alarms and power consumption, and enabling real-time action execution while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different detection devices (sound, image, thermal, video) are integrated into a single monitoring system, then detection capabilities are enhanced, but device complexity increases and false detection rates rise due to non-related sensor data
Solution Approach 1:
The patent combines multiple detection devices (sound sensor, image sensor, thermal sensor, video sensor) into a single integrated monitoring device with a unified processor that receives and processes all sensor inputs simultaneously, enabling enhanced detection capabilities while managing system complexity through integration
Solution Approach 2:
The processor is designed to perform multiple functions including receiving data from all sensor types, analyzing each data type, determining whether detected objects match target objects across different modalities, and generating alerts, making the device universally applicable for comprehensive monitoring
2Measurement precision
If sensor data is sent to a central server for analytical processing to enhance detection accuracy, then detection accuracy improves, but notification time is delayed due to intermediate communication
Solution Approach 1:
The patent extracts the analytical processing function from a remote central server and implements it locally within the monitoring device's processor, which performs real-time analysis of sensor data and generates alerts without requiring communication delays to external servers
Solution Approach 2:
The monitoring device performs self-analysis through its integrated processor that autonomously processes sensor data, identifies target objects, and generates alerts independently without external server intervention, enabling real-time response
3Measurement precision
If image data is transmitted to a remote central server, then analytical processing can be enhanced, but personal privacy rights may be violated
Solution Approach 1:
The patent extracts the data processing function from remote servers and implements it locally within the monitoring device, so that sensor data including image data is analyzed and processed within the device itself without being transmitted to external servers, thereby protecting privacy while maintaining analytical capability
4Speed
If conventional monitoring devices operate continuously to monitor objects, then real-time detection is achieved, but power consumption is high
Solution Approach 1:
The patent implements periodic sensing where the processor selectively activates sensors and processes data at intervals or triggered by specific conditions rather than continuous operation, reducing power consumption while maintaining real-time detection capability through efficient duty cycling
Data Source
AI summary
An artificial intelligence (AI)-enabled device including a sensor unit, an AI analysis unit, and an action execution unit, for detecting and monitoring objects and their activities within an operating field, is provided. The sensor unit captures multi-modal sensor data elements including sound, image, thermal, radio wave, and other environmental data associated with the objects along with timing data in the operating field. The AI analysis unit includes one or more AI analyzers that, in communication with an AI data library, receive and locally analyze each and an aggregate of the multi-modal sensor data elements. Based on the analysis, the AI analyzers distinguish between the objects detected and identified in the operating field, distinguish non-related sensor data, determine and monitor the activities of the identified objects, and generate and validate activity data from the activities. The action execution unit executes one or more actions in real time based on the validation.