Sensor-Based Object Tracking With User Association and Anomaly Detection
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Solution Overview
Problem
Existing location tracking solutions for personal items and equipment in factory environments often require costly RFID tags and are inefficient in detecting specific users, leading to time-consuming searches and misplaced items.
Innovation Solution
A computer-implemented method using machine learning to track objects and users without RFID tags, utilizing sensor data from microphones, cameras, and Wi-Fi routers, combined with knowledge bases and unsupervised learning to identify objects, associate them with users, and detect anomalies in their locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RFID tags are used for tracking personal items, then location tracking accuracy is improved, but cost and complexity increase
Solution Approach 1:
The patent extracts the tracking function from the object itself and places it in the environment. Instead of tagging items with RFID, the system uses sensors (cameras, microphones, Wi-Fi) deployed in the environment to detect and track objects, thereby eliminating the need for complex RFID tags while maintaining tracking capability
Solution Approach 2:
The patent introduces sensor data as an intermediary between the object and the tracking system. Sensors capture data about objects and their locations, and machine learning models process this intermediary data to identify objects and track them, replacing the direct RFID object-to-system connection
2Measurement precision
If RFID tags are attached to items, then object identification is improved, but manual pairing and setup time increases
Solution Approach 1:
The system performs self-service by automatically identifying objects and associating them with users without manual pairing. The machine learning model processes sensor data autonomously to recognize objects, determine their locations, and link them to user profiles, eliminating the need for manual setup time
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models with extensive data about objects, locations, and user behaviors. This pre-training enables the system to automatically identify and track objects without requiring manual configuration or pairing when actual tracking begins
3Loss of information
If existing tracking solutions are used, then location data is collected, but anomaly detection capability is insufficient
Solution Approach 1:
The system implements feedback by continuously monitoring object locations and comparing them against learned patterns and user expectations. The machine learning model provides feedback about abnormal behaviors (such as objects being left in unusual places or moved without expected user interaction), enabling reliable anomaly detection based on the collected location information
Data Source
AI summary
There is provided a method comprising: acquiring (110) sensor data related to an object; using the first learning module, identifying (120) the object based on the acquired sensor data using a first learning module and determining (130) a user associated with the identified object; determining (140) a timestamped location of the object based on at least one of the acquired sensor data and one or more locations of the one or more sensors; performing (150) a first analysis to determine whether the current status of the object contains an anomaly based on one or more predefined rules stored in a knowledge base; performing (160) a second analysis to determine whether the current status of the object contains an anomaly, using a second learning module; and validating (170) whether the current status of the object contains an anomaly based on results of the first analysis and results of the second analysis.