Machine Vision Task Analysis for Adaptive Worker Training
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Solution Overview
Problem
Generating training videos for worker tasks in industrial environments is nontrivial and requires room for improvement, especially in optimizing task performance and resource allocation.
Innovation Solution
A system and method utilizing machine vision and machine learning to analyze worker performance data from sensors, compare it to reference videos, and generate optimized training videos or feedback to improve task performance, while also optimizing task orchestration and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training videos are manually created and updated, then video content can be produced, but the process is nontrivial and requires substantial effort and time
Solution Approach 1:
The system automatically generates and updates training videos by capturing sensor data from the industrial environment and synthesizing videos without human intervention. The computing system self-services the entire video production process, from data collection to video generation and distribution, eliminating manual creation efforts
Solution Approach 2:
The patent replaces manual mechanical video production processes with automated computing systems that use sensor data and machine learning algorithms to generate videos. This substitution transforms the mechanical process of manual video creation into an automated digital process
2Productivity
If traditional training methods are used, then workers can be trained, but task performance optimization and resource allocation remain suboptimal
Solution Approach 1:
The computing system performs multiple functions: it captures sensor data, analyzes worker performance, generates training videos, optimizes task orchestration, and allocates resources. This multi-functional system replaces multiple separate processes with a single integrated platform
Solution Approach 2:
The system continuously monitors worker performance through sensors and uses this feedback to automatically update and improve training videos. The feedback loop ensures that training content evolves based on actual performance data, continuously optimizing task performance
3Adaptability or versatility
If performance variations from reference videos are not analyzed, then training content remains static, but performance improvements cannot be captured and propagated
Solution Approach 1:
The system uses automated machine learning algorithms and computer vision to detect and measure performance variations, replacing manual analysis methods. This substitution enables precise, objective measurement of subtle performance differences that would be difficult to detect manually
Solution Approach 2:
The system creates updated training videos by copying and adapting proven high-performance techniques identified through analysis. Successful performance patterns are replicated across the workforce through automatically generated training content
Data Source
AI summary
An exemplary system is configured to access data captured by one or more sensors in an industrial environment, the data comprising videos of operatives in the industrial environment. The system is further configured to determine, based on the data, a task performed by the operatives and to identify a video within the videos that represents a performance of the task comprising a variance from a reference performance of the task represented in a reference video associated with the task. The system is further configured to generate, based on the video and the variance, an output for improving the performance of the task or the reference performance of the task.