Camera-Based Child Detection for Laundry Appliance Safety
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Solution Overview
Problem
Conventional washing machines lack an effective method to accurately detect and prevent the presence of living things, such as children or pets, during wash cycles, posing safety risks.
Innovation Solution
A laundry appliance equipped with a camera assembly and a controller that uses image processing algorithms and machine learning image recognition to detect movement within the wash chamber, implementing responsive actions to prevent the start of wash cycles if living things are present.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional washing machines use simple door locking mechanisms without detection systems, then the device complexity is low, but the safety and reliability are insufficient to prevent children or pets from being trapped
Solution Approach 1:
The system performs preliminary detection of living things in the wash chamber before the door is locked and the wash cycle commences. The camera assembly captures images and the controller analyzes them to detect movement or presence of children or pets, preventing the door from locking until the chamber is confirmed safe. This preliminary action ensures safety is verified in advance, resolving the contradiction between maintaining simple mechanisms and ensuring reliable safety prevention.
2Reliability
If washing machines implement detection systems to identify living things, then the safety and reliability improve, but the device complexity increases due to additional components
Solution Approach 1:
The patent replaces complex mechanical detection systems with an optical-based camera assembly and electronic image processing. Instead of using mechanical sensors or complex physical detection mechanisms, the system uses a camera to capture images and electronic algorithms to detect movement or presence of living things. This substitution reduces mechanical complexity while maintaining high reliability in safety detection.
Solution Approach 2:
The camera assembly and controller work together in an integrated manner where the camera captures images and the controller automatically processes them to detect living things. The system serves itself by having the controller directly analyze the camera output without requiring external detection devices, simplifying the overall system architecture while ensuring reliable safety monitoring.
3Ease of operation
If washing machines use quick burst of water or drain pump to startle children, then the ease of operation is improved, but the measurement precision and reliability of detecting living things deteriorates
Solution Approach 1:
The patent replaces physical disturbance methods (water bursts, drain pump activation) with optical detection using a camera assembly. Instead of relying on mechanical or hydraulic actions to detect presence, the system uses image capture and electronic analysis to precisely identify living things. This substitution dramatically improves detection accuracy while maintaining ease of operation through automated image processing.
Solution Approach 2:
The camera assembly serves as an intermediary between the wash chamber environment and the controller. Rather than directly interacting with children or pets through water or mechanical means, the camera captures visual information that the controller then analyzes to detect movement or presence. This intermediary approach enables precise, non-invasive detection that does not rely on startling or disturbing the detected objects.
Data Source
AI summary
A laundry appliance includes a basket rotatably mounted within a cabinet and defining a chamber configured for receiving a load of clothes, and a camera assembly mounted within the cabinet in view of the chamber. A controller of the laundry appliance implements a method for detecting living things in the chamber and initiates a responsive action, for example, by disturbing the chamber by adjusting at least one operating parameter of the laundry appliance, obtaining one or more images of the chamber using the camera assembly, and determining that there is movement in the chamber based at least in part on the one or more images using an image processing algorithm and a machine learning image recognition process.