Wearable Mask AI Control for Real-Time Tool Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack effective methods for remote training and real-time control of tools using artificial intelligence and machine learning, particularly in vocational settings where workers need guidance from remote experts.
Innovation Solution
A wearable mask equipped with an edge processor and AI agent that receives sensory data to identify work settings and generates control instructions for tools, enabling real-time interaction and guidance through video, audio, and haptic feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a wearable mask with edge processor and AI agent is used to enable real-time tool control and expert guidance, then training efficiency and quality control are enhanced, but device complexity increases
Solution Approach 1:
The wearable mask serves as an intermediary device between the worker and the remote expert system. It captures sensory data from the work environment, processes it through an embedded AI agent, and transmits relevant information to remote experts while receiving control instructions back. This intermediary role enables real-time collaboration and guidance without requiring direct physical presence of experts, thereby improving training efficiency while managing system complexity through modular architecture.
Solution Approach 2:
The system replaces traditional mechanical training methods (in-person supervision, physical demonstrations) with electronic and software-based solutions. The edge processor and AI agent substitute for human experts in real-time analysis and decision-making, while wireless communication replaces physical presence. This substitution reduces the need for complex human coordination and enables scalable training operations.
2Productivity
If sensory data collection and AI processing are implemented in real-time, then task performance efficiency is improved, but use of energy increases
Solution Approach 1:
The AI agent is pre-trained with extensive knowledge about work environments, tools, and safety protocols before deployment. This preliminary training allows the system to make rapid decisions during actual operations without requiring intensive real-time computation for basic recognition tasks. The edge processor leverages this pre-computed knowledge to efficiently process sensory data, reducing energy consumption during task execution while maintaining high productivity.
Data Source
AI summary
In one embodiment, a computer-implemented method includes receiving, at a wearable mask, first information pertaining to a work setting, wherein the first information comprises video, audio, haptic feedback, or some combination thereof, determining, using an edge processor communicatively coupled to the wearable mask and based on the first information, one or more first characteristics of the work setting, generating, using the edge processor and based on the one or more first characteristics of the work setting, one or more first control instructions configured to modify one or more first operating parameters of a tool, and transmitting, to the tool, the one or more first control instructions to modify the one or more first operating parameters of the tool.