AR Assistance System Dynamic Guidance via Neural Network State Prediction
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Solution Overview
Problem
Existing augmented reality systems fail to provide effective guidance due to their inability to capture and adapt to external factors, leading to inadequate instructions that may not account for changes in the task environment, thus hindering user performance.
Innovation Solution
A system utilizing an Artificial Neural Network (ANN) based action prediction model and augmented intelligence model to analyze real-time video streams and sensor data, dynamically determining the present state and external events to generate sequential instructions that adapt to changing conditions, incorporating both ANN and rule-based engines for instruction prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing AR systems provide fixed step-by-step guidance based on pre-programmed expert videos, then the system structure is simple and easy to implement, but the system cannot adapt to external factors and provides inappropriate guidance when task conditions change
Solution Approach 1:
The system transitions from static pre-programmed instructions to dynamic adaptive guidance by continuously monitoring task state through computer vision and sensor data. The instruction prediction model dynamically generates updated instructions based on real-time state assessment, allowing the system to adapt to changing external factors while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system implements feedback mechanisms by capturing real-time video streams and sensor data to assess current task state. This feedback loop enables the system to compare actual progress against expected progress and generate appropriate corrective instructions, resolving the contradiction between adaptability and complexity through intelligent feedback processing.
2Measurement precision
If the system captures and processes real-time video streams and sensor data using ANN models to determine present state and external events, then the guidance accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex task monitoring process into distinct functional modules: video stream acquisition, sensor data collection, present state determination using ANN, external event detection, and instruction generation. This segmentation allows each module to be optimized independently, achieving high measurement precision while managing computational complexity through distributed processing.
Solution Approach 2:
The system performs preliminary actions by pre-training ANN models with extensive task data before deployment. This preliminary training enables the models to quickly and accurately assess task state during actual execution without requiring complex real-time computations, thus achieving high precision while reducing operational computational burden.
3Adaptability or versatility
If the system provides guidance based on pre-divided steps from expert videos, then the implementation is straightforward, but the system fails to capture external factors that may affect task completion
Solution Approach 1:
The system achieves multi-functionality by integrating multiple data acquisition capabilities (video streaming, sensor data collection) and multiple analysis functions (state determination, event detection) into a unified platform. This universal system can capture diverse external factors across different tasks while maintaining straightforward implementation through standardized processing pipelines.
Data Source
AI summary
The disclosure relates to system and method for providing assistance to a user using augmented reality. The method includes acquiring a video stream and a set of data associated with a task being performed by a user, in real-time, using a camera and/or a sensor device. The video stream includes sequential frames. The method further includes determining a present state associated with the task based on the sequential frames using an Artificial Neural Network (ANN) based action prediction model; determining scenarios and events corresponding to the scenarios based on the video stream and the set of data using an ANN based augmented intelligence model; and determining sequential instructions required for assisting the user to accomplish the task, dynamically, based on the present state and the events associated with the task, using at least one of a rule-based engine and an ANN based instruction prediction model.