Machine Learning Hazard Detection via Trusted Trainee Evaluations
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Solution Overview
Problem
Conventional worksite safety systems rely on manual and costly data collection for machine learning, which limits their effectiveness in detecting and preventing safety hazards due to the high expense of gathering and filtering large training datasets, and human error remains a significant concern despite mandatory safety training.
Innovation Solution
A system that leverages safety training curriculum to generate refined data for machine learning by identifying trusted evaluators through gamification, allowing for the automatic detection and response to safety hazards by training a machine learning function with high-trust user evaluations of both known and unknown hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection methods are used to train machine learning systems for worksite safety, then the system can detect safety hazards, but the cost and time required for data collection becomes prohibitively expensive
Solution Approach 1:
The system allows trainees to self-generate training data by evaluating safety scenarios as part of their regular training curriculum. Instead of requiring external manual data collection, the training process itself produces the training data needed for the machine learning system, making the system self-sufficient in data generation.
Solution Approach 2:
Trainees evaluate safety scenarios and provide hazard assessments during their training before the machine learning system is deployed. This preliminary evaluation creates a dataset that is then used to train the ML system, so the data collection happens in advance as part of curriculum development rather than requiring ongoing manual collection.
2Measurement precision
If large amounts of training data are collected manually to improve machine learning performance, then hazard detection accuracy improves, but the cost of data filtering and classification becomes prohibitively expensive
Solution Approach 1:
The system automatically processes and filters training data generated by trainee evaluations using machine learning algorithms. The ML system autonomously classifies hazard scenarios and filters relevant training examples without requiring expensive manual filtering and classification processes.
Solution Approach 2:
The patent replaces manual mechanical processes of data filtering and classification with automated machine learning algorithms. The ML system automatically processes trainee evaluations, identifies relevant hazard patterns, and generates training datasets without human intervention in the data processing stage.
3Adaptability or versatility
If comprehensive safety training curriculum is continuously updated to cover new worksite issues, then safety training effectiveness improves, but the expense of creating and updating curriculum content increases
Solution Approach 1:
The system uses machine learning to analyze trainee evaluations and automatically identify emerging hazard patterns and gaps in existing curriculum content. This feedback loop enables the system to recommend specific curriculum updates based on actual training data, allowing continuous improvement without requiring expensive manual curriculum review and development processes.
Solution Approach 2:
The machine learning system proactively identifies when curriculum updates are needed by analyzing training data for emerging hazard patterns before they become widespread issues. This allows organizations to update curriculum content in advance based on predictive analytics rather than reacting to incidents, reducing the overall cost of curriculum maintenance.
4Reliability
If workers physically monitor the worksite to detect safety hazards, then real-time hazard detection is achieved, but the process becomes manually intensive and expensive
Solution Approach 1:
The patent replaces manual physical monitoring by workers with an automated machine learning system that processes images and video from workplace cameras. The ML system automatically detects hazards in real-time without requiring human workers to physically monitor the worksite, thereby maintaining safety monitoring effectiveness while eliminating manual labor requirements and associated costs.
Data Source
AI summary
Systems and methods for enhanced collection of training data for machine learning to improve worksite safety and operations. One embodiment is a system with an interface to receive first evaluations of a first scene from a group of trainees, the first scene belonging to safety curriculum content and depicting a worksite with a known hazard that is associated in memory with a hazard profile. The system includes a controller to determine a trusted subgroup of the trainees that correctly identified the known hazard in the first scene. The interface receives second evaluations of a second scene from the trusted subgroup of the trainees that depicts the worksite with an unknown hazard. The controller trains a machine learning function based on the second evaluations from the trusted subgroup of the trainees for automatic identification of hazard indications in the second scene depicting the worksite with the unknown hazard.