Real-Time Assessment of the Cross-Task Mental Workload Using Physiological Measures During Anomaly Detection

2018 ◽  
Vol 48 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Guozhen Zhao ◽  
Yong-Jin Liu ◽  
Yuanchun Shi
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Mental Workload ◽  
Physiological Measures ◽  
The Cross ◽  
Time Assessment

Using Pupillometry to Indicate the Cognitive Redline

2016 ◽  
Vol 60 (1) ◽  
pp. 685-685
Author(s):  
Carolina Rodriguez-Paras ◽  
Shiyan Yang ◽  
Thomas K. Ferris
Keyword(s):  
Real Time ◽  
Pupil Diameter ◽  
Mental Workload ◽  
Task Demands ◽  
Autonomous Nervous System ◽  
Cognitive Resources ◽  
Physiological Measures ◽  
Physiological Indicators ◽  
Increased Risk ◽  
Potential Indicator

Physiological measures, which are influenced by the arousal of the autonomous nervous system, have been studied as indicators of extreme levels of mental workload that approach or exceed the cognitive “redline”, the point at which task demand exceeds the supply of cognitive resources. In response to increasing task demands, measures such as heart rate variability show asymptotic patterns in arousal that are consistent with plateau patterns in subjective self-reported measures. This suggests potential to use physiological indicators in real time to predict when an operator is at increased risk of cognitive overload. Expanding on prior work, the current study examined pupil diameter as a new potential indicator of the cognitive redline in a multitask environment created with the Multi-Attribute Task Battery-II (MATB-II). Results showed that pupil diameter is sensitive to imposed mental workload and exhibits a similar asymptotic pattern that may provide another potential real-time indicator of the cognitive redline.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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