Emission characteristics of volatile organic compounds from consumer spray products based on product type, spray method, and distance.

Consumer spray products (CSPs) are widely used in daily life, yet it is challenging to find products that fully disclose all components posing health risks. Existing studies primarily focus on product components or VOC quantities emitted during use. Therefore, this study aimed to measure the VOC concentrations emitted by CSPs at varying distances. 47 CSPs available in the Korean market were selected, spanning three spray groups: antiseptics/insecticides (11), aromatic deodorants (16), and coating/polishing agents (20). VOC in air samples were collected using Tenax TA tube at a distance of 1 and 3 m from the sprayed CSPs and then analyzed by thermal desorption-gas chromatography-mass spectrometry system. Discrepancies were found between labeled and actual product components. Aromatic deodorants exhibited the highest total VOCs (TVOCs), while antiseptic/insecticide sprays exhibited the lowest. In the antiseptic/insecticide group and coating/polishing agent group, benzene as a propellant had a maximum concentration (30.9 ± 25.6 ppb), and as trigger, its concentration was 33.7 ± 30.7 ppb. Quantitative analysis using advanced analytical instruments only explained 26.1 ± 20.4% of toluene-equivalent TVOCs, suggesting the presence of additional substances. Concentrations varied by distance due to substance volatility and usage. Maintaining a distance of at least 1 m from CSPs is recommended.

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.

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.

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.

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.

Exploiting GPUs in virtual machine for BioCloud.

Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sharing mechanism of GPUs among VMs, they cannot achieve enough performance for biological applications of which computation throughput is more crucial rather than sharing. The proposed system exploits the pass-through mode of PCI express (PCI-E) channel. By making each VM be able to access underlying GPUs directly, applications can show almost the same performance as when those are in native environment. In addition, our scheme multiplexes GPUs by using hot plug-in/out device features of PCI-E channel. By adding or removing GPUs in each VM in on-demand manner, VMs in the same physical host can time-share their GPUs. We implemented the proposed system using the Xen VMM and NVIDIA GPUs and showed that our prototype is highly effective for biological GPU applications in cloud environment.

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.

Relations between ac-dc components and optical path length in photoplethysmography.

Photoplethysmography is used in various areas such as vital sign measurement, vascular characteristics analysis, and autonomic nervous system assessment. Photoplethysmographic signals are composed of ac and dc, but it is difficult to find research about the interaction of photoplethysmographic components. This study suggested a model equation combining two Lambert-Beer equations at the onset and peak points of photoplethysmography to evaluate ac characteristics, and verified the model equation through simulation and experiment. In the suggested equation, ac was dependent on dc and optical path length. In the simulation, dc was inversely proportionate to ac sensitivity (slope), and ac and optical path length were proportionate. When dc increased from 10% to 90%, stabilized ac decreased from 1 to 0.89 ± 0.21, and when optical path length increased from 10% to 90%, stabilized ac increased from 1 to 1.53 ± 0.40.

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.

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.

A real-time ECG data compression and transmission algorithm for an e-health device.

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

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.

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.

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.

Ideal filtering approach on DCT domain for biomedical signals: index blocked DCT filtering method (IB-DCTFM).

We proposed Index-Blocked Discrete Cosine Transform Filtering Method (IB-DCTFM) to design ideal frequency range filter on DCT domain for biomedical signal which frequently exposed to specific frequency noise such as motion artifacts and 50/60 Hz powerline interference. IB-DCTFM removes unwanted frequency range signal on time domain by blocking specific DCT index on DCT domain. In simulation, electrocardiography, electromyography, photoplethysmography are used as a signal source and FIR, IIR and adaptive filter are used for comparison with proposed IB-DCTFM. To evaluate filter performance, we calculated signal-to-noise ratio and correlation coefficient to clean signal of each signal and filtering method respectively. As a result of filter simulation, average signal to noise ration and correlation coefficient of IB-DCTFM are improved about 75.8 dB/0.477, and FIR, IIR and adaptive filtering results are 24.8 dB/0.130, 54.3 dB/0.440 and 29.5 dB/0.200 respectively.

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.

A design of the u-health monitoring system using a Nintendo DS game machine.

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

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.

Adaptive threshold method for the peak detection of photoplethysmographic waveform.

Photoplethysmography (PPG)-based temporal analyses have been widely used as a useful analytical method in physiological and cardiovascular diagnosis. Most of temporal approaches of PPG are based on detected peak points, peak and foot of PPG. The aim of presented study is the development of improved peak detection algorithm of PPG waveform. The present study demonstrates a promising approach to overcome respiration effect and to detect PPG peak. More extensive investigation is necessary to adapt for the cardiovascular diseases, whose PPG morphology has different form.

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.