Systems and methods using image recognition processes and determined device orientation for improved operation of a laundry appliance
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Solution Overview
Problem
Conventional washing machines require user input to select load types, often leading to inaccurate cycle settings, and existing sensor-based solutions increase complexity and cost without ensuring detection accuracy.
Innovation Solution
A method using a remote device's camera and angle sensors to capture images and determine the load attributes through machine learning, allowing for accurate wash cycle adjustments without additional sensing assemblies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor-based detection systems are installed in the washing machine to automatically detect load attributes, then automatic load detection capability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent introduces a smartphone as an intermediary device that performs the complex image processing and machine learning analysis externally, while the washing machine itself only needs simple camera and speaker components. This transfers the computational complexity from the appliance to the user's existing device, achieving automatic load detection without complicating the washing machine's internal systems.
Solution Approach 2:
The system creates a visual copy of the load through camera imaging and processes this copy externally using machine learning algorithms on the smartphone. Instead of installing complex sensors directly in the washing machine, the system captures optical information and analyzes it separately, then communicates results back to control the wash cycle.
2Ease of operation
If conventional user-input methods are used to select load types, then ease of operation is maintained, but measurement precision of load attributes deteriorates
Solution Approach 1:
The system enables the load itself to 'identify' its characteristics by being captured in an image. The machine learning algorithm automatically analyzes the visual properties of the clothes in the drum and determines fabric type, color, and other attributes without requiring the user to manually categorize or input information about the load.
Solution Approach 2:
The patent replaces manual user input mechanisms with an automated optical detection system. Instead of relying on users to select from preset categories or input data, the system uses camera imaging combined with machine learning to automatically and precisely identify load characteristics, substituting mechanical/button-based interaction with intelligent visual analysis.
3Measurement precision
If additional sensing assemblies are installed in the washing machine to detect load characteristics, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The smartphone serves as a universal device that handles multiple functions: it captures images via its camera, processes them through machine learning algorithms, communicates results to the washing machine, and even provides visual feedback to guide proper camera positioning. This multi-functional approach eliminates the need for specialized sensing assemblies dedicated to each detection task.
Solution Approach 2:
The system uses the smartphone as an intermediary between the washing machine and the load characteristics. Rather than installing multiple specialized sensors in the appliance, the smartphone acts as an external processing unit that analyzes load properties and communicates findings back to the washing machine controller, simplifying the appliance's internal architecture.
Data Source
AI summary
A method of operating a washing machine appliance may include obtaining one or more images of the washing machine appliance from a camera assembly of a remote device spaced apart from the cabinet. The method may also include receiving a plurality of angle readings from the remote device and determining a position of the remote device relative to the washing machine appliance based on the plurality of angle readings. The method may further include determining a set camera angle for the camera assembly is met based on the determined position of the remote device. The method may still further include analyzing at least one obtained image using a machine learning image recognition process, estimating a load attribute of a load of clothes within the washing machine appliance based on the analysis, and directing a wash cycle within the washing machine appliance based on the estimated load attribute.