Wearable Sensor System for Real-Time Worker Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current workplace injury monitoring systems are inefficient in collecting and analyzing data from wearable devices to identify safety risks in real-time, leading to delayed interventions and inconsistencies in reporting, which results in continued avoidable injuries despite various safety measures.
Innovation Solution
A wearable device system equipped with sensors and a monitoring system that evaluates sensor data to identify events of interest, communicates relevant data to a central system for analytics, and uses machine learning algorithms to assess worker physicality and safety risks, providing prioritized reviews and data analytics for proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wearable devices collect and transmit all sensor data continuously, then data completeness for risk assessment is improved, but data transmission time and system complexity increase
Solution Approach 1:
The system extracts and transmits only relevant sensor data windows containing identified events of interest, rather than continuously transmitting all sensor data. This selective extraction reduces data transmission time while maintaining data completeness for risk assessment by focusing on critical information.
Solution Approach 2:
The system transmits partial data (only windows containing events of interest) rather than complete continuous data streams. This partial action approach reduces overall data transmission volume and time while still providing sufficient information for effective safety risk assessment.
2Measurement precision
If multiple sensors and analytics processes are deployed, then measurement precision of worker physicality and safety risk is improved, but device complexity increases
Solution Approach 1:
The system segments the safety monitoring function into wearable devices worn by individual workers, which independently perform local analytics and identify events of interest. This segmentation distributes computational complexity across multiple simple units rather than requiring one complex centralized system, improving measurement precision while managing overall system complexity.
Solution Approach 2:
Each wearable device performs self-service analytics by autonomously evaluating sensor data to identify events of interest and determining which data windows to transmit. This self-service capability reduces the complexity of centralized processing while maintaining high measurement precision through distributed intelligence.
3Device complexity
If workers manually report safety concerns, then system simplicity is maintained, but reporting consistency and reliability deteriorate
Solution Approach 1:
The system replaces manual worker reporting (mechanical human process) with automated sensor-based detection and analytics. Wearable devices automatically identify events of interest and transmit data for risk assessment, eliminating inconsistencies associated with manual reporting while maintaining system simplicity through standardized automated processes.
Solution Approach 2:
The system implements automated feedback loops where sensor data is continuously evaluated, events of interest are identified, and risk assessments are generated and communicated. This automated feedback mechanism ensures consistent and reliable safety monitoring without requiring manual intervention, resolving the contradiction between system simplicity and reporting reliability.
Data Source
AI summary
A system and method for evaluating safety risk of workers is presented. The system includes wearable devices configured to be attached to or carried by workers during a work shift. The wearable device includes sensors configured to sample motion data and/or other sensor data indicative of working conditions and work performed by workers. In one or more arrangements, the wearable device evaluates sensor data to identify instances when sensor data satisfies a set of criteria indicative of events of interest and communicates portions of sensor data including identified instances of events of interest to a monitoring system. In one or more arrangements, the monitoring system is configured to evaluate the sensor data to quantify physicality exhibited by workers during a work shift.