Worker Fatigue Assessment Using Speech and Contextual Data
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Solution Overview
Problem
Monitoring worker fatigue in industrial settings is difficult due to the complexity of factors indicating fatigue onset, and existing methods can be cumbersome and intrusive, posing safety risks and reducing productivity.
Innovation Solution
A learning-based model that utilizes machine learning to analyze speech, telemetry, and video data from workers to identify patterns associated with fatigue, sending alerts and mitigation suggestions when fatigue thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fatigue monitoring methods are used, then fatigue detection capability is provided, but the monitoring process becomes cumbersome and intrusive
Solution Approach 1:
The system enables workers to self-report fatigue levels through mobile device inputs (surveys, ratings, or free-text responses). This self-service approach allows fatigue monitoring without external intervention, eliminating the need for intrusive observers or complex monitoring equipment while maintaining detection capability.
Solution Approach 2:
The patent replaces traditional mechanical monitoring methods (such as direct observation by supervisors or physical fatigue tests) with electronic data collection and machine learning algorithms. The system uses software-based analysis of worker inputs, speech patterns, and contextual data to detect fatigue, substituting mechanical processes with computational ones that are less intrusive.
2Reliability
If comprehensive fatigue monitoring is implemented, then safety is improved, but system complexity increases
Solution Approach 1:
The system uses a single mobile device to perform multiple functions: collecting self-reported fatigue data, capturing speech patterns, gathering contextual information about work conditions, and transmitting data to the server. This multi-functional approach consolidates what would otherwise require multiple separate monitoring systems into one universal platform, reducing overall system complexity.
Solution Approach 2:
The mobile device serves as an intermediary between the worker and the fatigue analysis system. Instead of directly complex sensors monitoring workers, the mobile device collects and pre-processes data, then transmits it to the server for machine learning analysis. This intermediary layer simplifies the architecture by separating data collection from complex analysis functions.
3Productivity
If real-time fatigue assessment is provided, then productivity is maintained, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data collection and organization at the mobile device and server level before fatigue analysis is needed. Worker inputs, speech data, and contextual information are gathered and pre-processed in advance, so that when real-time assessment is required, the machine learning model receives prepared data rather than raw unprocessed inputs, reducing computational energy requirements.
Solution Approach 2:
The system collects more data than strictly necessary (excessive action) including speech patterns, self-reports, and contextual information beyond basic fatigue metrics. This excess data provides the machine learning model with redundant information that improves assessment accuracy while distributing processing load, allowing real-time decisions without overwhelming computational demands at any single point.
Data Source
AI summary
A method is disclosed for providing real-time assessment of worker fatigue using a learning-based model, the method comprising: receiving speech data from a worker in a work environment and contextual information regarding the worker and the work environment; identifying patterns in the received speech data and the contextual information that correspond to an onset of fatigue in the worker; determining a probability of an occurrence of the onset of fatigue based on the identified patterns in the received speech data and contextual information; and sending an alert and/or a report to a real-time dashboard of a display when the computed probability of an occurrence of the onset of fatigue is above a predetermined threshold.