Worker Risk Assessment System Using Motion and Non-Motion Data
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Solution Overview
Problem
Current methods focus primarily on treating and reducing workplace injuries after they occur, rather than preventing them, leading to increased healthcare costs and a need for proactive risk assessment tools.
Innovation Solution
A fully connected worker risk assessment system that processes both motion and non-motion data using sensors and algorithms to provide holistic risk assessments, offering actionable insights to employers and workers to prevent injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If companies focus on treating and reducing injuries after they occur, then injury treatment effectiveness is improved, but healthcare costs increase and prevention capability deteriorates
Solution Approach 1:
The system performs preliminary risk assessment by collecting motion data from sensors, analyzing worker techniques before injuries occur, and providing feedback to prevent injuries proactively rather than reacting after injuries happen
Solution Approach 2:
The system implements continuous feedback loops where motion sensors monitor worker movements in real-time, analyze risk patterns, and provide actionable insights to workers and employers to modify behaviors and prevent injuries before they occur
2Measurement precision
If comprehensive motion and non-motion data is collected and processed, then risk assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses multi-functional sensors that simultaneously capture motion data, environmental data, and worker biometric data, processing all these diverse data types through a unified risk assessment algorithm to evaluate injury risk across multiple dimensions
Solution Approach 2:
The system introduces an intermediary data processing layer that aggregates raw sensor data from multiple sources, standardizes different data formats, and transforms complex multi-source data into simplified risk scores and actionable insights that are easy to interpret
Data Source
AI summary
In one aspect, a device agnostic system for providing a fully connected worker risk assessment is disclosed. The system includes an interface and data model for capturing and interpreting one or more variables associated with a worker's body motion and non-body motion related data. The system further includes a processor for aggregating, assessing, and interpreting the one or more input variables to perform a fully connected worker risk assessment based on not only the worker's movement, but also non-movement related data. The processor further provides an output based on the processed one or more input variables to provide a fully connected risk assessment and solutions relating to the same.