Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl
Preferences help
Polish version English version Language
enabled [disable] Abstract
Number of results

Results found: 2

Number of results on page
first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  Electroencephalogram (EEG)
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

100%
Sugiono S., Prasetya R. P., Fanani A. A., Cahyawati A. N.
Acta Neuropsychologica
|
2022
|
vol. 20(1)
1-15
EN
Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.
2
Content available remote

Estimating mental fatigue based on electroencephalogram and heart rate variability

86%
Zhang C., Yu X.
Polish Journal of Medical Physics And Engineering
|
2010
|
vol. 16
|
issue 2
67-84
EN
The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue.Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.