Worksite Safety Training Data Collection With Trusted User Capture
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Solution Overview
Problem
Companies face challenges in implementing machine learning for worksite safety due to the high cost of collecting, filtering, and classifying large amounts of training data, leading to reliance on manual and potentially outdated human monitoring and response methods.
Innovation Solution
A system that leverages safety training curriculum data to refine examples of hazardous situations through weighted evaluator trust, using a gamification system to incentivize user input, which is then used to improve machine learning systems for real-time hazard detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning is implemented for worksite safety detection, then detection accuracy and automation are improved, but the cost and complexity of collecting and classifying training data increases significantly
Solution Approach 1:
The system allows workers to self-report safety observations through a mobile application during their normal work activities. Workers capture images and describe hazards they encounter, which are then automatically processed by the machine learning system. This eliminates the need for specialized data collection teams and reduces system complexity while maintaining automation benefits.
Solution Approach 2:
A mobile application serves as an intermediary between workers and the machine learning system. The application provides a user-friendly interface for workers to submit safety observations and handles data preprocessing, validation, and transmission to the machine learning model, simplifying the overall data collection architecture.
2Ease of manufacture
If manual data collection methods are used, then implementation cost is reduced, but detection precision and comprehensiveness deteriorate
Solution Approach 1:
The system replaces manual review and classification of safety data with an automated machine learning model that processes images and text submissions. The model automatically identifies hazards, classifies them by type and severity, and prioritizes responses, significantly improving detection precision while maintaining ease of implementation through automated processing.
Solution Approach 2:
The system implements feedback loops where machine learning model predictions are continuously refined based on worker confirmations and corrections. When workers review system-generated hazard identifications, their feedback is used to retrain and improve the model's precision over time, creating a self-improving system that maintains high accuracy.
3Reliability
If comprehensive safety training curriculum is updated continually, then safety knowledge completeness is improved, but training cost and time investment increase
Solution Approach 1:
The machine learning model continuously analyzes incoming safety observations and proactively identifies emerging hazard patterns and trends before they become widespread issues. The system prepares draft curriculum updates and new training scenarios in advance based on these predictions, allowing safety teams to review and approve content more efficiently, thus maintaining knowledge completeness while reducing development time.
Solution Approach 2:
The safety training curriculum is transformed from a static document to a dynamic, continuously evolving system. The machine learning model automatically generates new training scenarios and updates existing content based on real-time analysis of worksite hazards, ensuring the curriculum remains current and comprehensive without requiring extensive manual updates.
4Measurement precision
If large amounts of training data are collected, then machine learning model accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system implements a two-stage processing approach where the machine learning model first performs rapid filtering to identify and prioritize the most critical and representative safety observations. Only these high-value data points undergo extensive processing and model training, reducing overall processing time while maintaining or improving model accuracy by focusing on the most informative examples.
Solution Approach 2:
The data processing pipeline is segmented into multiple independent stages: initial filtering, validation, enrichment, and model training. Each stage processes only the data required for its specific function, parallelizing operations where possible and reducing the computational burden on any single processing step, thereby decreasing total processing time while preserving model accuracy.
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 instruct a user device of a user to capture first sensor data of an object performing one or more known actions. The system also includes a training controller to allocate points as an award to the user of the user device for correctly capturing the object performing the one or more known actions, and to generate an instruction for the user to capture second sensor data of the object performing one or more unknown actions if the user has exceeded a threshold of awarded points. The training controller trains a machine learning function based on the second sensor data of the object performing the one or more unknown actions as identified by the user having exceeded the threshold of awarded points.