This study proposes an algorithm for removal of CPR artifact in order that automated external defibrillator (AED) can effectively diagnose ECG rhythm during cardiopulmonary resuscitation (CPR). Current AED required to interrupt chest compression for reliable rhythm analysis to avoid the effect of artifacts produced by CPR. However even temporarily interruption of chest compression during CPR adversely affects the probability of restoration of spontaneous circulation (ROSC) and survival after the delivery of the shock. Therefore, we proposed a method for removal of CPR artifacts using least mean square (LMS) filter. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. It was tested on 31 segments of shockable and 300 segments of non-shockable ECG signals recorded from three pigs during CPR. In the result, sensitivity (Se) and specificity (Sp) analysis on the test segments showed values of Se = 3.2%, Sp = 66.0% and Se = 96.8%, Sp = 98.7% in the case of unfiltered and filtered signals during CPR. In conclusion, it was shown that the proposed method can be a useful tool to exactly diagnose the ECG rhythm during the CPR.

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

In this paper we present an implantable pressure sensor to measure real-time blood pressure by monitoring mechanical movement of artery. Sensor is composed of inductors (L) and capacitors (C) which are formed by microfabrication and direct bonding on two biocompatible substrates (quartz). When electrical potential is applied to the sensor, the inductors and capacitors generates a LC resonance circuit and produce characteristic resonant frequencies. Real-time variation of the resonant frequency is monitored by an external measurement system using inductive coupling. Structural and electrical simulation was performed by Computer Aided Engineering (CAE) programs, ANSYS and HFSS, to optimize geometry of sensor. Ultrafast laser (femto-second) cutting and MEMS process were executed as sensor fabrication methods with consideration of brittleness of the substrate and small radial artery size. After whole fabrication processes, we got sensors of $3mm{\times}15mm{\times}0.5mm$. Resonant frequency of the sensor was around 90 MHz at atmosphere (760 mmHg), and the sensor has good linearity without any hysteresis. Longterm (5 years) stability of the sensor was verified by thermal acceleration testing with Arrhenius model. Moreover, in-vitro cytotoxicity test was done to show biocompatiblity of the sensor and validation of real-time blood pressure measurement was verified with animal test by implant of the sensor. By integration with development of external interrogation system, the proposed sensor system will be a promising method to measure real-time blood pressure.

Tear is a promising biological fluid for non-invasive health monitoring. It has been studied in the past to be a possible candidate for the diagnosis of certain systemic diseases, such as breast cancer, multiple sclerosis, prostate cancer, and diabetes. However, currently existing methods for collecting and extracting tear from the human eye causes inconsistencies in the biomolecule concentrations of the tear sample due to the irritating nature of the process. In response, we designed and fabricated a microfluidic system embedded soft contact-lens for the purpose of tear sampling. The lens was then tested with artificial tear for its tear sampling capability, and found to be able to find concentration equilibrium within 50 minutes. Additionally, simulation was carried out to further optimize the design so that tear sampling rate matched the natural tear turn-over rate of 1 microliter per minute.

In this study, a co-axial flow induced microfluidic chip to fabricate pure collagen type I microfiber via the control of collagen type I and Na-alginate gelation process. The pure collagen type I microfiber was generated by selective degradation of Ca-alginate from 'Core-Shell' structured hydrogel microfiber. To make 'Core-Shell' structure, collagen type I solution was introduced into core channel and 1.5% Na-alginate solution was injected into side channel in microfluidic chip. To evaluatethe 'Core-Shell' structure, the red and green fluorescence substances were mixed into collagen type I and Na-alginate solution, respectively. The fluorescence substances were uniformly loaded into each fiber, and the different fluorescence images were dependent on their location. By immoblizing EpH4-Ras and C6 cells within collagen type I and Na-alginate solution, we sucessfully demonstrated the co-culture of EpH4-Ras and C6 cells with 'Core-Shell' like hydrogel microfiber for 5 days. Only to produce pure collagen type I hydrogel fiber, tri-sodium citrate solution was used to dissolve the shell-like Ca-alginate hydrogel fiber from 'Core-Shell' structured hydrogel microfiber, which is an excellent advantage when the fiber is employed in three-dimensional scaffold. This novel method could apply various application in tissue engineering and biomedical engineering.

This study examined the effectiveness of micro-current stimulation (MCS) to improve the facial skin qualities by performing clinical test. The MCS is generally known that provide healing response in the damaged tissue and pain relief through activating the adenosine triphosphate (ATP) and protein synthesis. In here, we can hypothesize that the improvement of facial skin qualities may occur due to MCS, since the induction of micro-current from outside may enhance the cellular activity according to ATP activation. From the clinical test, our results showed that a variety of evaluating categories, which is able to assess the skin qualities, significantly enhanced due to stimulation of micro-current after 7 and 14 days. Therefore, we can estimate that MCS in human facial skin may be effective to improve the skin qualities.