In this study, joint angles of the lower extremity and inertial sensor data such as accelerations and angular velocities were measured during a ski simulator exercise in order to evaluate the skill of skiers. Twenty experts and twenty unskilled skiers were recruited for the study. All expert skiers held the certificates issued by the Korea Ski Instructors Association. A three-dimensional motion capture system and two inertial sensors were used to acquire joint movements, heel acceleration and heel angular velocity during ski simulator exercises. Pattern variation values were calculated to assess the variations in ski simulator motion of expert and unskilled skiers. Integral ratio of roll angular velocity was calculated to determine the parallel alignment of the two feet. Results showed that ski experts showed greater range of motion of joint angle, peak-to-peak amplitude(PPA) of heel acceleration and PPA of heel angular velocity than unskilled skiers. Ski experts showed smaller pattern variations than unskilled skiers. In addition, the integral ratio of roll angular velocity in ski experts was closer to 1. Inertial sensor data measurements during the ski simulator exercises could be useful to evaluate the skill of the skier.

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.

It is being tried to control robot arm using brain signal in the field of brain-machine interface (BMI). This study is focused on applying guidance laws for efficient robot arm control using 3D coordinates obtained from Magnetoencephalography (MEG) signal which represents movement of upper limb. The 3D coordinates obtained from brain signal is inappropriate to be used directly because of the spatial difference between human upper limb and robot arm's end-effector. The spatial difference makes the robot arm to be controlled from a third-person point of view with assist of visual feedback. To resolve this inconvenience, guidance laws which are frequently used for tactical ballistic missile are applied. It could be applied for the users to control robot arm from a first-person point of view which is expected to be more comfortable. The algorithm which enables robot arm to trace MEG signal is provided in this study. The algorithm is simulated and applied to 6-DOF robot arm for verification. The result was satisfactory and demonstrated a possibility in decreasing the training period and increasing the rate of success for certain tasks such as gripping object.

In sleep monitoring system, polysomnography (PSG) is the gold-standard but previous studies revealed that attaching numerous amount of sensors disturb sleep during the test which is the fundamental disadvantage of PSG. We suggest an unconstrained rapid-eye-movement (REM) sleep monitoring method measured with polyvinylidene (PVDF) film-based sensor for the normal and the obstructive sleep apnea (OSA) patients. Nine normal subjects and seventeen OSA patients have participated in the study. During REM sleep, rate and variability of respiration are known to be greater than in other sleep stages. Based on this phenomena, respiratory signals of participants were unconstrainedly measured using the PVDF-based sensor with the PSG and REM sleep were extracted from the average rate and variability of respiration. In epoch-by-epoch REM sleep detection, proposed method classified REM sleep with an average sensitivity of 72.3%, specificity of 92.5%, accuracy of 88.9%, and kappa statistic of 0.60 compared to the results of PSG. Student's t-test showed no significant difference between the results of normal and OSA group. This method is potentially applicable to REM sleep detection in homing environment or ambulatory monitoring.

The purpose of the study was to identify the effect of laser parameters on photocoagulation to maximize safety and efficacy during varicose vein treatment. CW and pulsed modes at 1470 nm were initially compared as a function of power on bovine liver tissue. In the pulsed mode, various parameters including repetition rate, duty cycle, and irradiation time were compared to evaluate tissue response during thermal treatment. The results demonstrated that CW and pulsed modes yielded almost similar coagulation development possibly due to shorter irradiation time of 5 sec. Regardless of laser mode, both repetition rate and duty cycle presented constant coagulation rate whereas longer irradiation time facilitated coagulation process.

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.