Analisis Pengaruh Chronotype dan Body Mass Index (BMI) terhadap Tingkat Kantuk Pengemudi

One of the main causes of Indonesia's increasing accident rate is fatigue and drowsiness, which places Indonesia among the other countries with the highest accident rates. The elements that significantly affect fatigue and sleepiness are individual factors, namely chronotype and Body Mass Index (BMI). Fatigue and drowsiness are evaluated using EEG signal indicators; there have been several related studies that focused on fluctuations in EEG signals under certain operating conditions. However, few studies evaluated the relationship between chronotype and BMI affecting sleepiness. This study aimed to investigate the relationship between chronotype and BMI and tiredness. The KSS questionnaire and EEG signal indicator were used to evaluate sleepiness. The study findings indicate that the beta signal and the driver's BMI have a negative relationship, namely drivers with high BMI are more likely to be sleepy than drivers with low BMI. In chronotype, the moderate morning type shows an increase in Alpha and Theta power compared to other types, caused by circadian rhythms.Salah satu penyebab utama meningkatnya angka kecelakaan di Indonesia adalah kelelahan dan kantuk yang menempatkan Indonesia di antara negara lain dengan tingkat kecelakaan tertinggi. Adapun elemen yang secara signifikan memengaruhi kelelahan dan kantuk adalah faktor individu yaitu chronotype dan Body Mass Index (BMI). Kelelahan dan kantuk dapat diukur menggunakan indikator sinyal EEG yang mana telah terdapat dalam beberapa studi terkait yang berfokus pada fluktuasi sinyal EEG dalam kondisi operasi tertentu, tetapi hanya sedikit yang meneliti efek chronotype dan BMI pada rasa kantuk. Penelitian ini bertujuan untuk menyelidiki hubungan antara kronotipe dan BMI dengan kelelahan. Kuesioner KSS dan indikator sinyal EEG digunakan untuk mengevaluasi kantuk. Temuan penelitian menunjukkan bahwa sinyal beta dan BMI pengemudi memiliki hubungan yang negatif, yaitu pengemudi dengan BMI tinggi lebih cenderung mengantuk daripada pengemudi dengan BMI rendah. Pada chronotype, tipe moderate morning menunjukkan peningkatan power Alpha dan Theta dibandingkan tipe lainnya, yang disebabkan oleh ritme sirkadian

Classifying BCI signals from novice users with Extreme Learning Machine

Volume 15, Issue 1 Previous ArticleNext Article Classifying BCI signals from novice users with extreme learning machine Germán Rodríguez-Bermúdez / Andrés Bueno-Crespo / F. José Martinez-Albaladejo Published Online: 2017-07-07 | DOI: https://doi.org/10.1515/phys-2017-0056 OPEN ACCESS DOWNLOAD PDF Abstract Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.Ingeniería, Industria y Construcció

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers

Estimation of single trial ERPs and EEG phase synchronization with application to mental fatigue

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