Human-Machine Task Allocation via Cognitive State Monitoring
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Solution Overview
Problem
The cognitive state of human operators becomes a limiting factor in the performance of advanced systems, particularly in safety-critical applications like aircraft operation, leading to increased error likelihood and degraded system performance and safety due to factors like channelized attention, diverted attention, and workload issues.
Innovation Solution
A system and method that integrate human operators with machines by evaluating and determining the cognitive state of operators through multimodal signals, using sensors and state-classifiers to allocate tasks between the operator and the machine, optimizing task distribution based on psycho-physiological responses such as EEG, fNIRS, and other physiological measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If task allocation is based on operator cognitive state monitoring, then safety and reliability improve, but device complexity and measurement precision requirements increase
Solution Approach 1:
The system segments the complex task allocation decision into multiple independent components: (1) separate sensor modules for different physiological measurements (EEG, ECG, GSR, temperature), (2) independent state classifiers for different cognitive states (alert, drowsy, stressed, focused), and (3) a task allocation module that integrates these classifications. This segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The patent introduces an intermediary cognitive state classification system between the raw physiological signals and the final task allocation decision. The state classifiers act as mediators that translate complex multimodal sensor data into interpretable cognitive state labels, which then inform task allocation. This intermediary layer simplifies the decision-making process while improving reliability.
2Measurement precision
If multiple multimodal signals are monitored to determine cognitive state, then measurement precision improves, but device complexity and loss of information increase
Solution Approach 1:
The system merges multiple independent physiological measurement modalities (EEG for brain activity, ECG for cardiac activity, GSR for galvanic skin response, and temperature sensing) into a unified cognitive state assessment. By combining these complementary signals, the system achieves higher measurement precision for cognitive state classification than any single modality could provide alone.
Solution Approach 2:
The sensor array is designed with universal multi-functionality, where each sensor type serves multiple purposes in cognitive state monitoring. For example, the EEG system not only detects alertness levels but also identifies stress states and focused attention. This multi-functionality maximizes the information extracted from each sensor while avoiding redundant hardware.
3Productivity
If dynamic task allocation is implemented based on cognitive state, then productivity and efficiency improve, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system implements continuous feedback loops where physiological signals are constantly monitored, cognitive states are dynamically classified, and task allocations are adjusted in real-time based on current operator state. This feedback mechanism enables the system to adapt to changing cognitive conditions, maintaining optimal productivity while managing the complexity of real-time detection through iterative adjustment.
Solution Approach 2:
The patent employs preliminary action by pre-training the state classifiers with labeled cognitive state data before deployment. The system performs preliminary calibration sessions where operators provide ground-truth cognitive state labels during various task conditions, allowing the classifiers to learn and adapt to individual operator characteristics beforehand. This preliminary action reduces the difficulty of real-time detection during actual operation.
Data Source
AI summary
Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.