Large-scale Growth and Simultaneous Doping of Molybdenum Disulfide Nanosheets.

A facile method that uses chemical vapor deposition (CVD) for the simultaneous growth and doping of large-scale molybdenum disulfide (MoS2) nanosheets was developed. We employed metalloporphyrin as a seeding promoter layer for the uniform growth of MoS2 nanosheets. Here, a hybrid deposition system that combines thermal evaporation and atomic layer deposition (ALD) was utilized to prepare the promoter. The doping effect of the promoter was verified by X-ray photoelectron spectroscopy and Raman spectroscopy. In addition, the carrier density of the MoS2 nanosheets was manipulated by adjusting the thickness of the metalloporphyrin promoter layers, which allowed the electrical conductivity in MoS2 to be manipulated.

Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects.

BACKGROUND: The subjects in EEG-Brain computer interface (BCI) system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. METHODS: The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. RESULTS: In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p < 0.05) but also stable performance in all experiments. CONCLUSIONS: We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.

Characteristics of motor imagery based EEG-brain computer interface using combined cue and neuro-feedback.

In this paper, we evaluated BCI algorithm using CSP for finding out about realistic possibility of BCI based on CSP. BCI algorithm that was comprised of CSP and least square linear classifier was evaluated in 10 persons. According to the result of the experiment, the effect of combined cue and neurofeedback is evaluated. In case of combined cue, the correlation of combined cue and visual cue is higher than other conditions. And in case of neurofeedback, some subject is exceptional but general trend shows the performance improvement by neurofeedback.

Wearable sensor glove based on conducting fabric using electrodermal activity and pulse-wave sensors for e-health application.

Improvement of the quality and efficiency of health in medicine, both at home and the hospital, calls for improved sensors that might be included in a common carrier such as a wearable sensor device to measure various biosignals and provide healthcare services that use e-health technology. Designed to be user-friendly, smart clothes and gloves respond well to the end users for health monitoring. This study describes a wearable sensor glove that is equipped with an electrodermal activity (EDA) sensor, pulse-wave sensor, conducting fabric, and an embedded system. The EDA sensor utilizes the relationship between drowsiness and the EDA signal. The EDA sensors were made using a conducting fabric instead of silver chloride electrodes, as a more practical and practically wearable device. The pulse-wave sensor measurement system, which is widely applied in oriental medicinal practices, is also a strong element in e-health monitoring systems. The EDA and pulse-wave signal acquisition module was constructed by connecting the sensor to the glove via a conductive fabric. The signal acquisition module is then connected to a personal computer that displays the results of the EDA and pulse-wave signal processing analysis and gives accurate feedback to the user. This system is designed for a number of applications for the e-health services, including drowsiness detection and oriental medicine.

Ride comfort analysis with physiological parameters for an e-health train.

Transportation by train has numerous advantages over road transportation, especially with regard to energy efficiency, ecological features, safety, and punctuality. However, the contrast in ride comfort between standard road transportation and train travel has become a competitive issue. The ride comfort enhancement technology of tilting trains (TTX) is a particularly important issue in the development of the Korean high-speed railroad business. Ride comfort is now defined in international standards such as UIC13 and ISO2631. The Korean standards such as KSR9216 mainly address physical parameters such as vibration and noise. In the area of ride comfort, living quality parameter techniques have recently been considered in Korea, Japan, and Europe. This study introduces biological parameters, particularly variations in heart rate, as a more direct measure of comfort. Biological parameters are based on physiological responses rather than on purely external mechanical parameters. Variability of heart rate and other physiological parameters of passengers are measured in a simulation involving changes in the tilting angle of the TTX. This research is a preliminary study for the implementation of an e-health train, which would provide passengers with optimized ride comfort. The e-health train would also provide feedback on altered ride comfort situations that can improve a passenger's experience and provide a healthcare service on the train. The aim of this research was to develop a ride comfort evaluation system for the railway industry, the automobile industry, and the air industry. The degree of tilt correlated with heart rate, fatigue, and unrelieved alertness.

Classification of BMI control commands from rat's neural signals using extreme learning machine.

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

Accelerometer sensor module and fall detection monitoring system based on wireless sensor network for e-health applications.

This research implements a wireless accelerometer sensor module and an algorithm to determine an individual's posture, activity, and fall. An analog wireless, two-axis accelerometer sensor module was used. Using a wireless radiofrequency module, the accelerometer signal is displayed on a PC-based "Acceloger" (e-Digitalmed, Seoul, Korea) viewer program. The activities of daily living algorithm uses the AC components of the accelerometer signal to determine posture, activity, and fall, and posture while standing, sitting, lying, walking, running, and so on is determined by the DC component. The performance of 30 subjects was evaluated by assessing the algorithm and calculating the detection rate for postures, motions, and subjects. Using a wireless sensor network in an experimental space, subject's postures, motions, and fall monitoring system was implemented. In conclusion, this system can be applied to patients such as the elderly for activity monitoring and fall detection and also for sports athlete's exercise measurement and pattern analysis.

Development of an integrated module using a wireless accelerometer and ECG sensor to monitor activities of daily living.

The objective of this work was to develop a wireless electrocardiogram (ECG) and acceleration detector powered by an imbedded battery. The module has a connector for a three-lead ECG socket and an embedded three-axis accelerometer. It also has an a secure digital (SD) memory slot and can store all data with 2 GB memory. The system transmits the data using a wireless module to measure ECG and an activity transition value. Using the correlation between accelerometer signal and ECG signal, the system can determine the activity assessment precisely and also detect cardiovascular abnormality.

SMS application using EMG signal of clenching teeth for e-health communication.

A proposed system using a Short Message Service (SMS) application based on electromyography (EMG)-controlled human-computer interface method by clenching teeth was evaluated. This system consists of an EMG signal processing device and SMS Software module on the PC. We can use SMS by a simple combination of two clenching patterns. The purpose of this study is to evaluate easy and comfortable communication methods for the disabled and rehabilitation patients as an e-health tool.

Implementation of safety driving system using e-health and telematics technology.

This research aimed to develop a safety driving system using e-health and telematics technology. Biosignal sensors were installed in an automobile to check the driver's health status with an automatic diagnosis system providing health information to the driver. Measured data were sent to the e-health center through a telematics device, and a medical doctor analyzed these data, sending diagnosis and prescription information to the driver. This system recognizes the driver's sleeping, drinking impairment, excitability, and fatigue using biosensors. The system initially provides alerts in the automobile. It also controls the driving environment in the car, searches for a highway service area using Global Positioning System (GPS), and provides additional information for safety driving. If a car accident has occurred, it makes an emergency call to the nearest hospital, emergency center, and insurance company. A conceptual and prototype model for an imbedded system is presented with initial data for driver condition. Such a system could prevent car accidents caused by drivers driving while intoxicated and falling asleep at the wheel using the driver's biosignals measured by biosensors. The system can provide various e-health services using a telematics system to enhance the technical compatibility of the automobile.

Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network.

This research implements wireless accelerometer sensor module and algorithm to determine wearer's posture, activity and fall. Wireless accelerometer sensor module uses ADXL202, 2-axis accelerometer sensor (Analog Device). And using wireless RF module, this module measures accelerometer signal and shows the signal at ;Acceloger' viewer program in PC. ADL algorithm determines posture, activity and fall that activity is determined by AC component of accelerometer signal and posture is determined by DC component of accelerometer signal. Those activity and posture include standing, sitting, lying, walking, running, etc. By the experiment for 30 subjects, the performance of implemented algorithm was assessed, and detection rate for postures, motions and subjects was calculated. Lastly, using wireless sensor network in experimental space, subject's postures, motions and fall monitoring system was implemented. By the simulation experiment for 30 subjects, 4 kinds of activity, 3 times, fall detection rate was calculated. In conclusion, this system can be application to patients and elders for activity monitoring and fall detection and also sports athletes' exercise measurement and pattern analysis. And it can be expected to common person's exercise training and just plaything for entertainment.