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.
ADL means 'Activity of Daily Living' and literally the activity from everyday living. In the early days, the activity measurement system using accelerometer measures in one direction at one part. This method has an advantage that easy and quantitative measurement is possible using one sensor. But that is so simple method that precise activity assessment for various posture classifications in daily living is impossible [2]. For the study about the correlation between the human's movement and energy consumption, the method that measures 3 direction activity data using 3-axis accelerometer sensor is used. This method is better than using many sensors, but the classification for various human's movement is still impossible [5]. In this study, using accelerometer sensor module, we develop the algorithm that classify the wearer's posture and activity. And we implement the monitoring system based on wireless sensor network. For the performance assessment of developed accelerometer module, algorithm and monitoring system, the experiment for 30 subjects is executed. This research implements wireless accelerometer sensor module and algorithm to determine wearer's posture, activity and fall. Wireless accelerometer sensor module uses ADXL202, 2axis 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.
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.
BackgroundThe 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.MethodsThe 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.ResultsIn 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.ConclusionsWe 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.
Impact pressure due to sloshing is of great concern for the ship owners, designers and builders of the LNG carriers regarding the safety of LNG containment system and hull structure. Sloshing of LNG in a partially filled tank has been an active area of research with numerous experimental and numerical investigations over the past decade. In order to accurately predict the sloshing impact load, it is necessary to develop advanced numerical simulation tools which can provide accurate resolution of local flow phenomena including wave breaking, jet formation, gas entrapping and liquid-gas interactions. In the present study, a new numerical method is developed for the simulation of violent sloshing flow inside a three-dimensional LNG tank considering wave breaking and liquid-gas interaction. The sloshing flow inside a membrane-type LNG tank is simulated numerically using the Finite-Analytic Navier-Stokes (FANS) method. The governing equations for two-phase air and water flows are formulated in curvilinear coordinate system and discretized using the finite-analytic method on a non-staggered grid. Simulations were performed for LNG tank in transverse and longitudinal motions including horizontal, vertical, and rotational motions. The predicted impact pressures were compared with the corresponding experimental data. The validation results clearly illustrate the capability of the present two-phase FANS method for accurate prediction of impact pressure in sloshing LNG tank including violent free surface motion, three-dimensional instability and air trapping effects.
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