Machine Learning Control Panel Mapping for Contactless Device Control
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Solution Overview
Problem
Existing control systems for physical devices require direct user interaction through control panels, which can be problematic in scenarios where hygiene is a concern, such as disease transmission risks.
Innovation Solution
A system utilizing machine learning to identify and map visual elements of a control panel with corresponding operations, enabling contactless control through a user device by training models to recognize and interact with the control panel using a user's device camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a control panel is used for device control, then the device can be operated, but the risk of infection transmission increases due to direct touch contact
Solution Approach 1:
The patent introduces a camera as an intermediary device that captures images of the control panel, allowing the user to interact with the device remotely through a computing device. The camera acts as a mediator between the user and the physical control panel, eliminating direct contact while preserving control functionality.
Solution Approach 2:
The system creates a digital copy of the control panel by capturing its visual appearance through a camera. This visual copy is then displayed on a computing device screen, allowing the user to interact with a representation of the control panel rather than the physical object itself, thus avoiding contact with potentially contaminated surfaces.
2Measurement precision
If machine learning models are trained to recognize control panel visual elements, then contactless control accuracy improves, but system complexity increases
Solution Approach 1:
The system performs preliminary training of machine learning models offline to recognize control panel visual elements and map them to control operations. This pre-training phase separates the complex model development from the actual control operation, allowing the runtime system to simply apply the pre-trained models without needing complex real-time training mechanisms.
Solution Approach 2:
The patent replaces traditional mechanical control systems with a vision-based machine learning system. Instead of physical interaction with control panel elements, the system uses computer vision to detect and recognize visual elements, and machine learning models to interpret them, substituting mechanical control with intelligent visual processing.
3Adaptability or versatility
If multiple machine learning control operations models are trained for different control operations, then control functionality is enhanced, but training time and computational resources increase
Solution Approach 1:
The patent merges multiple control operations into a unified machine learning framework. Instead of training separate models for each control operation independently, the system trains models that can handle multiple control operations within a single integrated architecture, reducing overall training time and computational overhead while maintaining versatility.
Data Source
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AI summary
Systems and methods for machine learning assisted contactless control of a physical device are disclosed. A system 100 comprises at least one processor and memory storing instructions executable by the at least one processor, the instructions when executed cause the system to obtain a plurality of images 421 of a physical device 106; identify the physical device 106 and a control panel 218, 228, 238 of the physical device 106 using the one or more images 421; train, using supervised learning, a plurality of machine learning control operations models of a machine learning system 314, 420, to determine control operations of one or more components of the control panel 218; and deliver a trained control operations model of the plurality of control operations models to a user device 104, 225, 235 to facilitate contactless control of the physical device 106 by the user device 104, 225, 235.