AR Industrial Device Interaction Using Gesture and Gaze Control
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Solution Overview
Problem
Current augmented reality systems lack effective methods for users to interact with virtual industrial automation devices in a natural and intuitive manner, particularly in industrial settings, where gestures and voice commands are not efficiently detected and translated into actions within the AR environment.
Innovation Solution
An interactive augmented reality system that utilizes a head-mounted device to receive image and gesture data, allowing users to select, manipulate, and couple virtual industrial automation devices through gaze, grasping, pushing, pulling, rotating, and snapping gestures, while also supporting voice commands to simulate real-world interactions within the AR environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional AR interaction methods are used, then system complexity is reduced, but ease of operation deteriorates due to lack of natural gesture recognition
Solution Approach 1:
The patent replaces traditional mechanical control interfaces (buttons, switches, manual navigation) with gesture-based interaction. The system detects natural hand gestures through computer vision and machine learning algorithms, translating physical hand movements into digital commands for manipulating virtual industrial automation devices in the AR environment.
Solution Approach 2:
The patent introduces gesture recognition technology as an intermediary between the user and the AR system. This intermediary layer captures hand movements, interprets them through machine learning models, and converts them into meaningful commands, thereby enabling natural interaction without requiring users to learn complex control schemes.
2Ease of operation
If gesture detection is added to improve interaction, then ease of operation improves, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The patent makes the existing AR device multi-functional by integrating gesture recognition capabilities into the existing head-mounted display system. The same camera and processor used for rendering AR content are also utilized for capturing and analyzing hand gestures, allowing one device to perform multiple functions without proportionally increasing complexity.
Solution Approach 2:
The system employs machine learning models that are pre-trained to automatically recognize and interpret gesture patterns. Once trained, the system serves itself by autonomously detecting gestures and translating them into commands without requiring manual configuration or complex real-time processing, reducing the operational burden on the system architecture.
3Adaptability or versatility
If multiple gesture types are supported, then adaptability improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the gesture recognition process into distinct phases: capture (acquiring hand images via camera), processing (extracting features and comparing against trained models), and interpretation (mapping recognized gestures to specific commands). This segmentation allows each stage to be optimized independently, improving overall detection accuracy while managing complexity.
Solution Approach 2:
The system performs preliminary training of machine learning models with extensive gesture data before deployment. By pre-training the recognition algorithms with diverse gesture patterns during the development phase, the system is better prepared to accurately detect and distinguish between multiple gesture types during actual use, reducing detection difficulties.
Data Source
AI summary
A method may include receiving, via a processor, image data associated with a user's surrounding and generating, via the processor, a visualization that may include a virtual industrial automation device. The virtual industrial automation device may depict a virtual object within image data, and the virtual object may correspond to a physical industrial automation device. The method may include displaying, via the processor, the visualization via an electronic display and detecting, via the processor, a gesture in image data that may include the user's surrounding and the visualization. The gesture may be indicative of a request to move the virtual industrial automation device. The method may include tracking, via the processor, a user's movement, generating, via the processor, a visualization that may include an animation of the virtual industrial automation device moving based on the user's movement, and displaying, via the processor, the visualization via the electronic display.