Refrigerator Live Inventory Sensing Using ML-Based Object Tracking
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Solution Overview
Problem
Refrigerator inventory management is inefficient, as users often forget what items are stored, when they were placed there, and when they will expire or need replacement, due to limited progress in tracking and identifying contents.
Innovation Solution
A method and system that uses sensors and cameras to detect and track objects in a refrigerator, acquiring images to identify objects using a machine learning model trained with crowd-sourced data, enabling dynamic inventory management, object identification, and expiration tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional manual inventory management is used in refrigerators, then users can store items without additional systems, but users cannot effectively track what items are stored, when they were placed, or when they will expire
Solution Approach 1:
The refrigerator door assembly integrates multiple functions including sealing, insulation, and inventory management. Sensors and cameras are incorporated into the door structure to monitor stored items, combining the sealing function with inventory tracking to reduce information loss without proportionally increasing overall system complexity.
Solution Approach 2:
The system automatically detects and tracks inventory items using sensors and cameras, eliminating the need for manual tracking by users. The processor autonomously analyzes sensor data to identify stored items, their placement times, and expiration dates, providing self-service inventory management that prevents information loss.
2Productivity
If sensors and cameras are integrated into the refrigerator door assembly, then real-time inventory tracking is achieved, but the device complexity and manufacturing cost increase
Solution Approach 1:
The door assembly is divided into functional segments including the sensor array, camera module, processor unit, and sealing components. This segmentation allows for modular manufacturing and assembly, where each component can be produced separately and then integrated, improving ease of manufacture while maintaining real-time inventory tracking capabilities.
Solution Approach 2:
Sensors and cameras are pre-integrated into the door assembly during manufacturing rather than being added as separate components later. The door structure is designed with built-in mounting positions for these components, allowing inventory tracking functionality to be established before the refrigerator is deployed, thereby improving productivity without significantly complicating the manufacturing process.
3Measurement precision
If multiple sensors are used to detect object properties such as color, shape, and size, then object identification accuracy is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
Multiple sensor types including color sensors, proximity sensors, and cameras are merged into a single integrated detection system within the door assembly. The processor combines data from all these sensors to comprehensively identify object properties such as color, shape, and size, achieving high measurement precision while reducing the overall difficulty of detection through unified system architecture.
Solution Approach 2:
The processor acts as an intermediary that receives and integrates signals from multiple sensors. It processes the raw data from color sensors, proximity sensors, and cameras to accurately determine object properties, thereby improving identification accuracy while simplifying the detection process through centralized data processing rather than requiring direct complex measurements.
4Loss of information
If the system continuously monitors and tracks objects in the refrigerator, then live inventory information is provided, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the sensor system operates periodically by triggering image capture and analysis only when the refrigerator door is opened or closed. This periodic operation maintains live inventory information availability by updating the system at relevant moments while significantly reducing energy consumption compared to continuous operation.
Solution Approach 2:
The system uses feedback from door opening/closing events to trigger inventory monitoring activities. When the door state changes, the processor is activated to capture and analyze images, updating the inventory database. This feedback-based triggering ensures information remains current while minimizing energy consumption by keeping the system in a low-power state between events.
Data Source
AI summary
A Live Inventory System and Process for use with an active object storage structure, such as a refrigerator. The disclosed System and Process includes a method for dynamically identifying an object being placed in or taken out of a refrigerator, the method comprising: detecting a motion of an object at the refrigerator using one or more sensors coupled with the refrigerator or sensing a refrigerator open condition; acquiring one or more images of at least a part of the object as the object is being placed inside the refrigerator or removed from the refrigerator; and using the acquired images, tracking the motion of the object, determining a direction of the motion of the object, and identifying the object using a trained ML (Machine Learning) model, the ML model trained, at least in part, using a crowd-based training method including acquisition of images from other refrigerators.