A framework of state identification for operational support based on task-phase and attentional-condition identification

2010 ◽  
Author(s):  
Mitsuhiro Kamezaki ◽  
Hiroyasu Iwata ◽  
Shigeki Sugano
Keyword(s):  
State Identification ◽  
Attentional Condition

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals State Identification of Transformer Under DC Bias Based on Wavelet Singular Entropy

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 58944-58952
Author(s):  
Jun Liu ◽  
Huarong Zeng ◽  
Wei Niu ◽  
Peilong Chen ◽  
Kui Xu ◽  
...  
Keyword(s):  
State Identification ◽  
Dc Bias

scholarly journals Effect of Distracting Background Speech in an Auditory Brain–Computer Interface

2021 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Álvaro Fernández-Rodríguez ◽  
Ricardo Ron-Angevin ◽  
Ernesto J. Sanz-Arigita ◽  
Antoine Parize ◽  
Juliette Esquirol ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Cognitive Workload ◽  
Auditory Event ◽  
Shared Attention ◽  
Attentional Condition ◽  
Different Types ◽  
The Impact ◽  
Auditory Bci

Studies so far have analyzed the effect of distractor stimuli in different types of brain–computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.

Lower-Limb Rehabilitation at Home

2021 ◽  
Vol 13 (2) ◽  
pp. 12-23
Author(s):  
Seanglidet Yean ◽  
Bu-Sung Lee ◽  
Chai Kiat Yeo
Keyword(s):  
Lower Limbs ◽  
Ageing Population ◽  
Joint Angles ◽  
Functional Activities ◽  
State Identification ◽  
Motion State ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation ◽  
Rehabilitation Exercise ◽  
At Home

Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.

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