Hoon Ko

Stress Effects CoCr₂O₄ Film on MgO and MgAl₂O₄ Grown by RF-Sputter Process

Multiferroic CoCr2O4 film was deposited on MgO and MgAl₂O₄ substrates by the rf-sputtering process. The films were prepared at an RF-magnetron sputtering power of 50 W and a pressure of 10 mtorr (20 sccm in Ar), and at substrate temperatures of 550 ℃. The crystal structure was determined to be a spinel (Fd-3m) structure by means of X-ray diffraction (XRD) with Cu K￥a radiation. The thickness and morphology of the films were measured by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The magnetic properties were measured using a Superconducting Quantum Interference Device (SQIUD) magnetometer. While the ferrimagnetic transitions were observed at about 93 K, which was determined as the Neel temperature, the magnetic properties all show different behaviors. The differences between the magnetic properties can be explained by the stress effects between CoCr₂O₄ and the substrates of MgO and MgAl₂O₄.

Smartphone-Based Cardiac Rehabilitation Program: Feasibility Study

We introduce a cardiac rehabilitation program (CRP) that utilizes only a smartphone, with no external devices. As an efficient guide for cardiac rehabilitation exercise, we developed an application to automatically indicate the exercise intensity by comparing the estimated heart rate (HR) with the target heart rate zone (THZ). The HR is estimated using video images of a fingertip taken by the smartphone’s built-in camera. The introduced CRP app includes pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up. In the exercise with intensity guidance, the app estimates HR from the pulse obtained using the smartphone’s built-in camera and compares the estimated HR with the THZ. Based on this comparison, the app adjusts the exercise intensity to shift the patient’s HR to the THZ during exercise. In the post-exercise period, the app manages the ratio of the estimated HR to the THZ and provides a questionnaire on factors such as chest pain, shortness of breath, and leg pain during exercise, as objective and subjective evaluation indicators. As a key issue, HR estimation upon signal corruption due to motion artifacts is also considered. Through the smartphone-based CRP, we estimated the HR accuracy as mean absolute error and root mean squared error of 6.16 and 4.30bpm, respectively, with signal corruption due to motion artifacts being detected by combining the turning point ratio and kurtosis.

Dedicated cardiac rehabilitation wearable sensor and its clinical potential

We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.

Wearable Photoplethysmographic Sensor Based on Different LED Light Intensities

Wearable photoplethysmographic sensors in the form of wristbands and watches have become increasingly popular in mobile healthcare. We propose a technique to cancel motion artifacts by using two reflective pulse signals from a single green LED, switched by a microcontroller between high and low light intensities. We accurately estimate motion artifacts by applying differential measurement and common-mode rejection. The performance was assessed for four different motion artifacts: artificial white noise, artificial color noise, and vertical/horizontal random movements of a hand. Under each different condition, we accurately cancelled out motion artifacts.

Multiple switching light sources based motion artifacts reduction in reflectance photoplethysmography

We proposed a technique to eliminate motion artifacts by subtracting the two green signals with different light intensity from single green LED. We used green LED light source with a photodetector to obtain the PPG signals from the wrist. We showed that the signal subtraction with different light intensity from the same light source reduced motion artifacts, and the clean PPG signal was remained. Performance comparison was carried out in five different scenarios: stationary state, fingers movement, gripping, up-down vertically bending and left-right horizontally swinging conditions. As expected, we obtained suitable clean PPG signals in all conditions using our proposed technique.We proposed a technique to eliminate motion artifacts by subtracting the two green signals with different light intensity from single green LED. We used green LED light source with a photodetector to obtain the PPG signals from the wrist. We showed that the signal subtraction with different light intensity from the same light source reduced motion artifacts, and the clean PPG signal was remained. Performance comparison was carried out in five different scenarios: stationary state, fingers movement, gripping, up-down vertically bending and left-right horizontally swinging conditions. As expected, we obtained suitable clean PPG signals in all conditions using our proposed technique.