Wearable Task Guidance Using Video-Based Operation State Detection
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Solution Overview
Problem
Existing training methods for operations often rely on static instructions and preconfigured materials, which may not account for rare or unique issues that inexperienced workers may encounter during task performance, leading to inefficiencies and resource wastage.
Innovation Solution
An operation management system utilizing a wearable device and an operation performance model trained on historical videos to identify next tasks and physical objects involved, providing real-time display data to facilitate more efficient task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static instructions and preconfigured training materials are used, then training structure is simple and easy to implement, but the system cannot adapt to rare or unique issues encountered during task performance
Solution Approach 1:
The system transitions from static training materials to dynamic real-time guidance by continuously analyzing video streams and providing adaptive instructions. The AR device dynamically adjusts displayed information based on the user's current task state, equipment conditions, and detected anomalies, making the training system flexible and responsive to unique situations.
Solution Approach 2:
The system enables self-service training by using AI algorithms to automatically analyze video data, identify task states, and provide guidance without requiring external trainers. The machine learning models autonomously process visual information and generate appropriate instructions, reducing dependency on human instructors while maintaining adaptability.
2Measurement precision
If real-time video processing and AI analysis are implemented, then task guidance accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames and extracting key features before full AI analysis. The AR device prepares visual data by identifying relevant objects and task states in advance, reducing the computational burden during real-time decision-making and lowering energy consumption while maintaining accuracy.
Solution Approach 2:
The system applies partial action by focusing AI analysis only on critical portions of the video stream rather than processing every frame in full detail. The AR device selectively analyzes key moments and regions where task state changes occur, reducing overall computational requirements while preserving identification accuracy.
3Productivity
If continuous monitoring and real-time feedback are provided, then operation performance is improved, but information processing load increases
Solution Approach 1:
The system extracts only the essential information needed for task guidance from the continuous video stream. The AR device identifies and isolates key visual features, task states, and critical anomalies, filtering out redundant data. This extraction approach reduces information processing load while maintaining the ability to provide effective real-time feedback.
Solution Approach 2:
The system implements feedback by providing targeted information based on detected task states rather than continuous exhaustive analysis. The AR device gives guidance only when task transitions occur or anomalies are detected, reducing processing load while maintaining improved operation performance through timely feedback.
Data Source
AI summary
A wearable device is disclosed. The wearable device comprises a camera, a display device, and a controller. The controller is configured to receive, from the camera, a video stream that depicts a user performing an operation and process, using an operation performance model, a set of frames of the video stream that indicates a state of a performance of the operation by the user. The controller is also configured to determine, based on the state of the performance by the user, a next task of the operation; obtain task information associated with performance of the next task of the operation; and cause, based on the task information, the display device to present instructional information associated with facilitating performance of the next task of the operation. The operation performance model is trained based on a plurality of historical videos indicative of historical performances of the operation by other users.