AI Edge Classifier for Sensor Position Tracking
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Solution Overview
Problem
Current sensor systems for home and office monitoring and security lack efficient methods to accurately classify and transmit position tracking events across various networks, leading to incomplete data consolidation and inadequate response to environmental changes.
Innovation Solution
The integration of 9-axis Inertial Measurement Units (IMUs) with artificial intelligence engines and wireless transceivers in sensor devices, enabling precise measurement of acceleration, orientation, and magnetic fields, and the use of edge classifier engines to process and transmit classified event data through various communication interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor systems use basic position tracking without AI classification, then device complexity is reduced, but measurement precision and reliability of position tracking events deteriorate
Solution Approach 1:
The system performs preliminary classification of position tracking events using AI/ML models before data transmission. The edge classifier engine pre-processes sensor data locally, identifying and categorizing relevant events (e.g., door openings, security breaches) before transmitting only classified event data to remote servers, thereby improving measurement precision while managing device complexity through intelligent data filtering
Solution Approach 2:
The sensor system is segmented into multiple functional components: local edge classifier engines for preliminary processing, wireless transceivers for communication, and remote servers for data consolidation. This segmentation allows AI classification to be distributed across the system, improving position tracking accuracy without concentrating all complexity in a single device
2Loss of information
If sensor systems transmit all position tracking data without classification, then data completeness is improved, but loss of time in data processing and transmission increases
Solution Approach 1:
The edge classifier engine extracts and isolates only the most relevant position tracking events from the complete sensor data stream. By taking out and categorizing significant events (security breaches, door openings) while filtering out redundant data, the system maintains essential information completeness while dramatically reducing data processing and transmission time
Solution Approach 2:
Data classification is performed preliminarily at the edge device before transmission. The AI model pre-sorts and prioritizes events locally, ensuring that critical position tracking information is identified and prepared for transmission in advance, reducing overall processing time while maintaining data completeness for relevant events
3Measurement precision
If sensor systems use comprehensive AI classification for all events, then measurement precision is improved, but use of energy for processing and transmission increases
Solution Approach 1:
The system applies AI classification partially rather than comprehensively to all sensor data. The edge classifier engine selectively classifies position tracking events based on their significance, applying full AI processing only to potentially relevant events while using lighter processing for routine movements, thereby maintaining measurement precision for critical events while reducing overall energy consumption
Solution Approach 2:
Different levels of classification accuracy are applied to different types of events based on their local importance. High-precision AI classification is applied locally to security-critical events like unauthorized access attempts, while standard processing handles routine position changes, optimizing energy usage while maintaining measurement precision where it matters most
4Productivity
If sensor systems implement edge classification engines, then productivity in data processing is improved, but device complexity increases
Solution Approach 1:
The sensor device incorporates multi-functional components that serve both simple sensing and complex AI classification functions. The edge classifier engine is integrated into the existing sensor architecture, allowing the same hardware platform to perform both basic position tracking and advanced event classification, thereby improving productivity without proportionally increasing device complexity
Solution Approach 2:
The AI classification functionality is nested within the existing sensor device architecture. The edge classifier engine operates as an integrated layer within the sensor system, with classification algorithms embedded in the processing unit, creating a nested structure where complex AI functions are contained within the simpler sensor framework, improving productivity while managing complexity through hierarchical integration
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
This solution provides enhanced accuracy in position tracking and event classification, enabling timely and appropriate responses to environmental changes, such as door openings or security breaches, by consolidating data effectively across networks.
Implementation Method 1
The sensor unit is configured to measure and report an output representative of a motion state for the sensor apparatus... the output signal includes information sufficient to determine one or more changes to an environment associated with the sensor apparatus
Implementation Method 2
9-axis Inertial Measurement Units (IMUs)... enabling precise measurement of acceleration, orientation, and magnetic fields
Implementation Method 3
9-axis Inertial Measurement Units (IMUs)... enabling precise measurement of acceleration, orientation, and magnetic fields
Data Source
AI summary
A sensor apparatus includes an interface configured to interface and communicate with a network, a sensor unit configured to measure and report an output representative of a motion state for the sensor apparatus, memory and processing circuitry configured to execute operational instructions to receive the output signal from the sensor unit, where the output signal is representative of one of a plurality of motion states for the sensor unit and the output signal includes information sufficient to determine one or more changes to an environment associated with the sensor apparatus. The processing circuitry is configured to classify, via an artificial intelligence model, the output signal according to previously classified events to produce a classified output determine whether to transmit a notification to the network.