Clinical observation using virtual reality for dental education on surgical tooth extraction: A comparative study.

BACKGROUND: Clinical observation conducted during the 3rd and 4th years of dental school is an important part of dental students' clinical education. However, conventional clinical observation is associated with several problems, including the lack of opportunity for all students to assist during surgery. Virtual reality (VR) technologies and devices can be used to demonstrate clinical processes that dental students need to learn through clinical observation. This study aimed to evaluate the effectiveness of teaching dental students the surgical tooth extraction procedure through clinical observation using VR. METHODS: We recruited third- and fourth-year dental students and divided them into a VR clinical observation group (VR group) and a conventional clinical observation group (control group). The control group visited an outpatient clinic and observed an oral and maxillofacial specialist perform surgical tooth extraction, whereas the VR group watched a 360° video of surgical tooth extraction using a head-mounted display. After observation, both groups were surveyed regarding their satisfaction with the clinical observation and their understanding of the procedure. RESULTS: Understanding of the procedure and satisfaction with the observation were significantly higher in the VR group than in the control group (p = 0.001 and p = 0.047, respectively). Compared with conventional clinical observation, VR clinical observation improved learning motivation and medical thinking and judgment skills; however, interaction between professors and students was lacking. CONCLUSIONS: VR clinical observation using 360° videos might be an effective teaching method for students. However, to allow interaction between professors and students during clinical observations, using it along with conventional clinical observation is necessary.

Cardiomyocytes from phorbol myristate acetate-activated mesenchymal stem cells restore electromechanical function in infarcted rat hearts.

Despite the safety and feasibility of mesenchymal stem cell (MSC) therapy, an optimal cell type has not yet emerged in terms of electromechanical integration in infarcted myocardium. We found that poor to moderate survival benefits of MSC-implanted rats were caused by incomplete electromechanical integration induced by tissue heterogeneity between myocytes and engrafted MSCs in the infarcted myocardium. Here, we report the development of cardiogenic cells from rat MSCs activated by phorbol myristate acetate, a PKC activator, that exhibited high expressions of cardiac-specific markers and Ca(2+) homeostasis-related proteins and showed adrenergic receptor signaling by norepinephrine. Histological analysis showed high connexin 43 coupling, few inflammatory cells, and low fibrotic markers in myocardium implanted with these phorbol myristate acetate-activated MSCs. Infarct hearts implanted with these cells exhibited restoration of conduction velocity through decreased tissue heterogeneity and improved myocardial contractility. These findings have major implications for the development of better cell types for electromechanical integration of cell-based treatment for infarcted myocardium.

Effect of spraying air freshener on particulate and volatile organic compounds in vehicles.

People in these days spend approximately 6 % of their time in a means of transport. Air fresheners are frequently used in vehicles to mask odors; however, they can cause adverse health effects such as cardiovascular disease, systemic inflammation and autonomic dysfunction. This study aimed to identify the effects of air fresheners on the concentrations of particulate and volatile organic compounds (VOCs) in different vehicle cabins. Scanning mobility and optical particle sizers were used for the particle measurements. VOCs (e.g., BTEX and d-limonene) were collected using a Tenax TA. The products were sprayed for less than a minute. The study assessed three spray products (all trigger types), vehicle size (small, medium, and large), cabin temperature (10 °C, 20 °C, and 25 °C), and in-vehicle ventilation mode (all-off, recirculation, and external inflow modes). The particle concentration increased rapidly during the 1-min spraying of the products. The proportion of nanoparticles in the front seat (67.2 % ± 2.2 %) was 11.1 % ± 2.2 % lower than that in the rear seat (75.6 % ± 2.1 %). The spray product and vehicle size did not significantly affect the particle or VOC concentrations. With an increase in the temperature of the front seat, the proportion of nanoparticles increased by 25.3 % ± 3.2 %. Moreover, the maximum total VOC concentrations (front seat: 364.3 µg/m3; back seat: 241.3 µg/m3) were observed at 20 °C. Under in-vehicle ventilation, recirculation effectively reduced the overall particle concentration within the cabin; however, the generated VOCs circulated. The external inflow proved effective in cabin air purification by reducing the total VOC concentration to 56.0-57.2 % compared with other ventilation modes. These findings provide substantial insight into the persistence of particles and the dynamics of their dispersion, thereby enabling informed decision-making for particle-related risk management.

Measurement of Airborne Particles and Volatile Organic Compounds Produced During the Heat Treatment Process in Manufacturing Welding Materials.

Background: There is little information about the airborne hazardous agents released during the heat treatment when manufacturing a welding material. This study aimed to evaluate the airborne hazardous agents generated at welding material manufacturing sites through area sampling. Methods: concentration of airborne particles was measured using a scanning mobility particle sizer and optical particle sizer. Total suspended particles (TSP) and respirable dust samples were collected on polyvinyl chloride filters and weighed to measure the mass concentrations. Volatile organic compounds and heavy metals were analyzed using a gas chromatography mass spectrometer and inductively coupled plasma mass spectrometer, respectively. Results: The average mass concentration of TSP was 683.1 ± 677.4 µg/m3, with respirable dust accounting for 38.6% of the TSP. The average concentration of the airborne particles less than 10 µm in diameter was 11.2-22.8 × 104 particles/cm3, and the average number of the particles with a diameter of 10-100 nm was approximately 78-86% of the total measured particles (<10 µm). In the case of volatile organic compounds, the heat treatment process concentration was significantly higher (p < 0.05) during combustion than during cooling. The airborne heavy metal concentrations differed depending on the materials used for heat treatment. The content of heavy metals in the airborne particles was approximately 32.6%. Conclusions: Nanoparticle exposure increased as the number of particles in the air around the heat treatment process increases, and the ratio of heavy metals in dust generated after the heat treatment process is high, which may adversely affect workers' health.

Comparison of sampling methods for the determination of volatile organic compounds in consumer aerosol sprays.

Many studies have evaluated the hazardous substances contained in various household chemical products. However, for aerosol spray products there is currently no international standard sampling method for use in a component analysis. The aim of this study was to develop an appropriate sampling method for the analysis of volatile organic compounds (VOCs) in consumer aerosol sprays. Two different sampling methods, spraying (into a vial) and perforating (and transferring the contents into a vial), were used to evaluate the levels of 16 VOC components in eight different aerosol spray products. All eight products contained trace amounts of hazardous VOCs, and a quantitative analysis showed that, for the same product, VOC concentrations were higher when spraying than when perforating. Using the spraying method, average toluene, ethylbenzene, p-xylene, o-xylene, and styrene concentrations were 1.80-, 2.10- 2.25-, 2.03-fold, and 1.28-fold higher, respectively, than when using the perforating method. The spraying method may provide more realistic estimates of the user's exposure to harmful substances and the associated health risks when using spray products. Of the two representative methods widely used to analyze harmful substances in consumer aerosol sprays, the spraying method is recommended over the perforating method for the analysis of VOCs.

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

BACKGROUND: Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. METHODS: In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. RESULTS: A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. CONCLUSIONS: The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

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.

Optimum conductive fabric sensor sites for evaluating the status of knee joint movements using bio-impedance.

BACKGROUND: There have been many studies that utilize the bio-impedance measurement method to analyze the movements of the upper and lower limbs. A fixed electrical current flows into the limbs through four standard disposable electrodes in this method. The current flows in the muscles and blood vessels, which have relatively low resistivity levels in the human body. This method is used to measure bio-impedance changes following volume changes of muscles and blood vessels around a knee joint. The result of the bio-impedance changes is used to evaluate the movements. However, the method using the standard disposable electrodes has a restriction related to its low bio-impedance changes: the standard disposable electrodes are only able to measure bio-impedance from a limited part of a muscle. Moreover, it is impossible to use continuously, as the electrodes are designed to be disposable. This paper describes a conductive fabric sensor (CFS) using a bio-impedance measurement method and determines the optimum configuration of the sensor for estimating knee joint movements. METHODS: The upper side of subjects' lower limbs was divided into two areas and the lower side of subjects' lower limbs was divided into three areas. The spots were matched and 6 pairs were selected. Subjects were composed of 15 males (age: 30.7 ± 5.3, weight: 69.8 ± 4.2 kg, and height: 173.5 ± 2.8 cm) with no known problems with their knee joints. Bio-impedance changes according to knee joint flexion/extension assessments were calculated and compared with bio-impedance changes by an ankle joint flexion/extension test (SNR I) and a hip joint flexion/extension test (SNR II). RESULTS: The bio-impedance changes of the knee joint flexion/extension assessment were 35.4 ± 20.0 Ω on the (1, 5) pair. SNR I was 3.8 ± 8.4 and SNR II was 6.6 ± 7.9 on the (1, 5) pair. CONCLUSIONS: The optimum conductive fabric sensor configuration for evaluating knee joint movements were represented by the (1, 5) pair.

The design of the m-health service application using a Nintendo DS game console.

In this article, we developed an m-health monitoring system using a Nintendo DS game console to demonstrate its utility. The proposed system consists of a biosignal acquisition device, wireless sensor network, base-station for signal reception from the sensor network and signal conversion according to Internet protocol, personal computer display program, and the Nintendo DS game console. The system collects three-channel electrocardiogram (ECG) signals for cardiac abnormality detection and three-axis accelerometer signals for fall detection of a person. The collected signals are then transmitted to the base-station through the wireless sensor network, where they are transformed according to the transmission control protocol/Internet protocol (TCP/IP) and sent to the destination IP through Internet network. To test the developed system, the collected signals were displayed on a computer located in different building through wired Internet network and also simultaneously displayed on the Nintendo DS game console connected to Internet network wirelessly. The system was able to collect and transmit signals for more than 24 h without any interruptions or malfunctions, showing the possibility of integrating healthcare monitoring functions into a small handheld-type electronic device developed for different purposes without significant complications. It is expected that the system can be used in an ambulance, nursing home, or general hospital where efficient patient monitoring from long distance is necessary.

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.

Robust algorithm for arrhythmia classification in ECG using extreme learning machine.

BACKGROUND: Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima. METHODS: In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat. RESULTS: The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively. CONCLUSION: The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.

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.

The e-health landscape: current status and future prospects in Korea.

The e-health infrastructure in Korea has come a long way within a short period since the establishment of the Korea e-Health Association in 2003 by the Korean Ministry of Commerce, Industry and Energy. Since then, four related government departments in the Ministry of Health and Welfare, Ministry of Information and Communication, Ministry of National Defense, and the Ministry of Science and Technology have also started planning and promoting their own strategies for e-Health applications. We have defined standardization, law and policy planning, human resources development, research and development for e-health products, and international collaboration as the five core pillars for the development of a successful e-health industry. In 2008, through the reorganization of government ministries, e-health has become an even more important growth industry for the Korean economy. Consequently, relevant government ministries have more concrete and robust action plans for realization of e-health in effect.

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.

Statistical approach to quantify the presence of phase coupling using the bispectrum.

The bispectrum is a method to detect the presence of phase coupling between different components in a signal. The traditional way to quantify phase coupling is by means of the bicoherence index, which is essentially a normalized bispectrum. The major drawback of the bicoherence index (BCI) is that determination of significant phase coupling becomes compromised with noise and low coupling strength. To overcome this limitation, a statistical approach that combines the bispectrum with a surrogate data method to determine the statistical significance of the phase coupling is introduced. Our method does not rely on the use of the BCI, where the normalization procedure of the BCI is the major culprit in its poor specificity. We demonstrate the accuracy of the proposed approach using simulation examples that are designed to test its robustness against noise contamination as well as varying levels of phase coupling. Our results show that the proposed approach outperforms the bicoherence index in both sensitivity and specificity and provides an unbiased and statistical approach to determining the presence of quadratic phase coupling. Application of this new method to renal hemodynamic data was applied to renal stop flow pressure data obtained from normotensive (N = 7) and hypertensive (N = 7) rats. We found significant nonlinear interactions in both strains of rats with a greater magnitude of coupling and smaller number of interaction peaks in normotensive rats than hypertensive rats.

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.

A study on a non-contacting respiration signal monitoring system using Doppler ultrasound.

We proposed non-contacting respiration signal monitoring system for sleep apnea syndrome. Experiments were conducted by emitting 40 kHz ultrasound beam, which is set tone burst mode by 1 ms period to a subject chest. Normal respiration condition and a simulated sleep apnea syndrome condition were measured while subjects were holding breath. To obtain the actual respiration signal from the raw signal, ultrasound attenuation characteristics were considered. The Doppler ultrasound signal was detectable once the received signal obtained by demodulation circuits passed through a low pass filter (LPF). The signal's ripples were eliminated by moving average method and the signal's peaks were detected by phase portrait reconstruction method to measure the respiration rate.