Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.

In this study, a monitoring system of cognitive state in usual behavior without restraint using wireless EEG, ECG and NIRS on developmental disorder children such as mental retardation was developed. By using this system we would like to investigate the education training effect on cognitive state in a mental retardation child in four years. The aim of special education is to make adaptations, accommodations and modification that allow a child with a mental retardation to succeed in classroom. This education training separated into two systems, resting state and studying state. After the measurement, we calculated the EEG power spectrum of alpha, theta bands and the low frequency power (LF), high frequency power (HF), and LH/HF value from the RR interval in ECG. At the same time we calculated the changes in concentration of oxyhemoglobin ([oxy-Hb]). The result in studying state showed theta power is lower than beta power in study state in the other hand beta power is lower than theta power in resting state From LF/HF activity we could know that sympathetic activity is increasing and from the result that has obtained from ECG, the parasympathetic activity is decreasing the time. NIRS showed the increasing in study states and decreasing in resting states at Fp2 area from oxyhemoglobin analysis.

Attention is a condition when someone concentrates on a specific task while ignoring other perceivable information. Numerous methods of attention level detection such as observation, self-assessment, and objective performance have been applied especially in supervised machine learning. But those methods tend to be delayed, sporadic, not at the moment in time, and based on participant cognitive ability. This study proposed a new labeling method for attention level detection by using quantitative evaluation formula based on blink rates and pupillometry. Comparison in error detection between self-assessment, observation, and objective performance has been done in this study. After that, this study investigated the effect of attention level based on self-assessment toward blink rates and pupillometry. The result shown blink rates in low attention is higher than high attention. On the other hand, pupillometry in low attention is smaller than high attention. The effect of attention levels toward pupillometry and blink rates are extracted into several algorithms. The result from experimental procedure shown quantitative evaluation formula has percentage error less than 15% compared with self-assessment. Overall, these results demonstrated that the proposed method can be used to be data labeling for other physiological signals such as electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS). After that, this quantitative formula was applied to EEG-ECG-NIRS for attention level detection. Two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS has been used to detect attention level during tasks load. Our result has shown the accuracy system 82.31%. INDEX TERMS Attention, blink rates, pupillometry, supervised machine learning, electroencephalograph (EEG), electrocardiograph (ECG), near-infrared spectroscopy (NIRS).

Heart Disease affects approximately 70 million people worldwide where most people do not even know the symptoms. This research examines the prototype of early warning system for heart disease by android application. It aims to facilitate users to early detect heart disease which can be used independently. To build the application in android phone, variable centered intelligence rule system (VCIRS) as decision makers and pulse sensor - Arduino as heart rate detector were applied in this study. Moreover, in Arduino, the heart rate will become an input for symptoms in Android Application. The output of this system is the conclusion statement of users diagnosed with either coronary heart disease, hypertension heart disease, rheumatic heart disease or do not get any kind of heart disease. The result of diagnosis followed by analysis of the value of usage variable rate (VUR) rule usage rate (RUR) and node usage rate (NUR) that shows the value of the rule that will increase when the symptoms frequently appear. This application was compared with the medical analysis from 35 cases of heart disease and it showed concordance between diagnosis from android application and expert diagnosis of the doctors.

Atensi adalah suatu kondisi ketika seseorang berkonsentrasi pada tugas tertentu sambil mengabaikan informasi lainnya. Penelitian pada artikel ini mengusulkan penggunaan pupilometri untuk evaluasi tingkat atensi. Pupilometri didefinisikan sebagai pengukuran perubahan ukuran pupil. Dalam studi ini, tingkat atensi didasarkan atas laporan dari peserta (self assessment) dalam stimulasi yang berdurasi 10 detik. Melalui penelitian ini, didapatkan hasil bahwa ukuran pupil menunjukkan tidak ada perbedaan signifikan di permulaan 6 detik stimulasi (P=0.4776), P>0.05. Perbedaan signifikan terjadi pada 4 detik terakhir dari stimulasi P<0.05.