Conference Paper

The Relation between Observers' Psychophysiological Conditions and Human Errors during Monitoring Task

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Abstract

In this research, we investigated the relation between observers' psychophysiological conditions and human errors made in the task of monitoring electrocardiogram (ECG) wave pattern changes. The number of errors and fingertip volume pulse waves were recorded during the task. Chaos analysis was applied to the fingertip volume pulse waves to calculate the largest Lyapunov exponents. The number of diagnostic errors of judgment and operation errors was treated as human errors. We found significant negative correlation between the largest Lyapunov exponents and human errors. The results showed that when the largest Lyapunov exponents were low, the observers made mistakes.

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... For all the experiments, we observed that higher error rates corresponded to lower LLEs, as shown in Figure 16. e line chart and bar chart represent the LLEs and the error rates, respectively [25]. ...
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This article is a comprehensive review of recent studies of the authors on the indication of mental health from biological information contained in pulse waves. A series of studies discovered that the largest Lyapunov exponent (LLE) of the attractor, which is constructed for the time series data from pulse waves, can provide as an effective indicator of mental health. A low level of LLE indicates insufficiency of external adaptability, which is characteristic of dementia and depression sufferers. On the contrary, a continuous high level of LLE indicates excessive external adaptability, and people in this condition tend to resort to violence. With this discovery, real-time display of the LLE, combined with other physiological indexes such as the autonomic nerve balance (ANB), sample entropy, and vascular age, as a reference, can enable people to conduct self-check of mental status. To this end, software development was performed in order to enable users to conduct pulse wave measurement anywhere at any time and display the analytical results in real time during the measurement.
... INCE it was discovered that a finger plethysmogram (FPG) shows chaotic fluctuations, several researchers have attempted to assess human psychophysiological states using a chaos analysis to the signals the organism emits [1][4] 1 . The chaos analysis is used to evaluate the chaotic fluctuation, which is expressed by the largest Lyapunov exponents (LLEs). ...
Conference Paper
We investigated the largest Lyapunov exponents (LLEs) of finger plethysmogram (FPG) and heart rate (HR) under anxiety, fear and relief states and compared each measured LLEs with the HR. We devised a task in which the participants experienced the emotions of anxiety, fear, and relief. FPG and HR were recorded during the task, and chaos analysis was applied to FPG to calculate the LLEs. Our results showed significant positive correlations between the LLEs of FPG and the degrees of anxiety and fear. In addition, the degree of relief showed a significant negative correlation with the LLEs of FPG. However, no significant correlation was observed between HR and the degrees of three emotions. These results suggest that the LLEs of FPG are a more sensitive psychological index than the HR.
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Conference Paper
We conducted a nonlinear analysis of fingertip pulse waves and found that the Lyapunov exponent referencing the “divergence” of chaotic attractor trajectory of fingertip pulse waves is an effective method for determining mental health in humans. In particular, we showed that this method is very effective for the early detection of dementia and depression, as well as in the detection of mental changes in healthy persons. In contrast, current measurement methods to determine mental health are subjective in most cases and are neither objective nor simple enough with respect to time and cost. The development of an apparatus allowing easy measurement for many users is therefore necessary. We illustrate the possibility of mental health self-checks using pulse wave divergence based on a series of examples in previous studies. In addition, we developed software to express the fluctuation of the Lyapunov exponent using time series data from multiple measurements. If changes in mental status can be assessed by studying the fluctuation factor of the Lyapunov exponent, we will be closer to effectively evaluating and controlling mental health problems.
... In chaotic datasets, attractor plots and 'divergence' of attractor trajectories are characterized by Lyapunov exponents. Previously, we focused on the Lyapunov exponent of pulse waves in research targeting persons of various ages and in various situations1234. Our results showed that to maintain mental health, it is important that there is harmony with the appropriate functioning of the sympathetic nervous system, which is associated with qualities such as the ability to interact with the external environment and society, flexibility, spontaneity, and cooperation. We also learned that the values representative of such harmony were associated with the maximum Lyapunov exponent (MLE) obtained from nonlinear analysis [5,6]. ...
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... The Lyapunov exponent is a property of chaotic systems that expresses the attractor and represents the "divergence" of the attractor trajectory. We focused on the Lyapunov exponent, which had not been quantitatively evaluated as an index of psychological changes in humans, and investigated its relationship with dementia and communication skill (an ADL index) in aged persons [1], its relationship with error rate at work [8], its relationship with diurnal changes and indices of cumulative fatigue in employees [5]- [7], and the time series fluctuations in divergence in 0-to 5-year-old children and the effect of parental affection towards the children [9]. ...
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Full-text available
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The exponential divergence or convergence of nearby trajectories (Liapunov exponents) is conceptually the most basic indicator of deterministic chaos. A new method is proposed to determine the spectrum of several Liapunov exponents (including positive, zero, and even negative ones) from the observed time series of a single variable. The method has been applied to various known model systems and also to the Rayleigh-Benard experiment, and the dependence of the Liapunov exponents on the Rayleigh number has been elucidated.
Chaotic Voice Analysis and Model of Cerebral Dynamics
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Development and Application of a System for Measuring the Error of Judgment and Operation in Monitoring Task
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Time Course of Errors and Lyapunov Exponents by Block for Six Patients Condtion
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Fig.8. Time Course of Errors and Lyapunov Exponents by Block for Six Patients Condtion.
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Development and Application of a System for Measuring the Error of Judgment and Operation in Fig.9. Time Course of Errors and Lyapunov Exponents by Block for Nine Patients Condtion
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