• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• Asian-Australasian Journal of Animal Sciences
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• 제30권9호
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• pp.1270-1277
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• 2017

Objective: The present study aimed to determine the impact of ewe body condition score (BCS) (over a range of 2.0 to 3.0) and nutritional treatments (consisting of differing herbage masses) during very late pregnancy and lactation and their potential interaction on the performance of twin-bearing ewes and their lambs to weaning. Methods: On day 142 of pregnancy, twin-bearing ewes with a BCS of 2.0, 2.5, or 3.0 were allocated to a "Moderate' or 'Unrestricted' nutritional treatment until day 95 of lactation (weaning). The nutritional treatments aimed to achieve average herbage masses of 1,200 to 1,300 kg dry matter (DM)/ha (Moderate) and 1,500 to 1,800 kg DM/ha (Unrestricted). Results: There were no three-way interactions between ewe BCS group, nutritional treatment and time for any ewe or lamb parameter. The nutritional treatments had no effect (p>0.05) on lamb birth or weaning weight. Lambs born to Moderate ewes had greater survival and total litter weight at weaning (p<0.05). Regardless of BCS group, Unrestricted treatment ewes had greater body condition and back-fat depth at weaning than Moderate treatment ewes (p<0.05). Ewes of BCS 2.0 group reared lighter lambs to weaning (p<0.05) and tended to have a lower total litter weight (p = 0.06) than BCS 3.0 group ewes. Conclusion: This study suggests farmers should aim to have all ewes with a BCS of 2.5 or 3 in late pregnancy for optimal lamb weaning performance. Furthermore, there is no benefit to lamb production of offering ewes pasture masses >1,200 kg DM/ha during very late pregnancy and lactation.

• 센서학회지
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• 제20권5호
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• pp.343-350
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• 2011

The compliance and stiffness of artery are closely related with disease of arteries. Pulse wave velocity(PWV) in the blood vessel is a basic and common parameter in the hemodynamics of blood pressure and blood flow wave traveling in arteries because the PWV is affected directly by the conditions of blood vessels. However, there is no standardized method to measure the PWV and it is difficult to measure. The conventional PWV measurement has being done by manual calculation of the pulse wave transmission time between coronary arterial proximal and distal points on a strip chart on which the pulse wave and ECG signal are recorded. In this study, a pressure sensor consisting of strain gauges is used to measure the blood pressure of arteries in invasive method and regular ECG electrodes are used to record the ECG signal. The R-peak point of ECG is extracted by using a reference level and time windowing technique and the ascending starting point of blood pressure is determined by using differentiation of the blood pressure signal and time windowing technique. The algorithm proposed in this study, which can measure PWV automatically, shows robust and good results in the extraction of feature points and calculation of PWV.

• 대한의용생체공학회:의공학회지
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• 제28권1호
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• pp.36-45
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• 2007

Today Human Personal Trainers are becoming very famous in this health conscious world. They teach user to achieve fitness goals in managed way. Due to their high fee and tight schedule they are unavailable to mass number of people. Another solution to this problem is to develop digital personal trainer portable instrument that may replace human personal trainers. We developed a portable digital exercise trainer device - BIOFIT that manages, monitors and records the user's physical status and workout during exercise session. It guides the user to exercise efficiently for specific fitness goal. It keeps the full exercise program i.e. exercises start date and time, duration, mode, control parameter, intensity in its memory which helps the user in managing his exercise. Exercise program can be downloaded from the internet. During exercise it continuously monitors the user's physiological parameters: heart rate, number of steps walked, and energy consumed. If these parameters do not range within prescribed target zone, the BIOFIT will alarm the user as a feedback to control exercise. The BIOFIT displays these parameters on graphic LCD. During exercise it continuously records the heart rate and number of steps walked every 10 seconds along with exercise date and time. This stored information can be used as treatment for the user by an exercise expert. Real-time ECG monitoring can be viewed wirelessly (RF Communication) on a remote PC.

• 대한의용생체공학회:의공학회지
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• 제28권4호
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• pp.494-502
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• 2007

Ubiquitous location based services (u-LBS) will be interested to an important services. They can easily recognize object position at anytime, anywhere. At present, many researchers are making a study of the position recognition and tracking. This paper consists of postion recognition and user identification system. The position recognition is based on location under services (LBS) using a signal strength map, a database is previously made use of empirical measured received signal strength indicator (RSSI). The user identification system automatically controls instruments which is located in home. Moreover users are able to measures body signal freely. We implemented the multi-hop routing method using the Star-Mesh networks. Also, we use the sensor devices which are satisfied with the IEEE 802.15.4 specification. The used devices are the Nano-24 modules in Octacomm Co. Ltd. A RSSI is very important factor in position recognition analysis. It makes use of the way that decides position recognition and user identification in narrow indoor space. In experiments, we can analyze properties of the RSSI, draw the parameter about position recognition. The experimental result is that RSSI value is attenuated according to increasing distances. It also derives property of the radio frequency (RF) signal. Moreover, we express the monitoring program using the Microsoft C#. Finally, the proposed methods are expected to protect a sudden death and an accident in home.

• Korean Journal of Acupuncture
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• 제28권4호
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• pp.79-89
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• 2011

Objectives : The aim of this study is to propose the W area of pulse (AW) as a new index which can confirm the arteriosclerosis by analyzing parameters of 5-level pressure pulse waveform measurement system for normotensive group according to aging. Methods : We measured radial pulse waveforms of normotensive group (20 to 60 years old) using 3-dimensional pulse imaging analyser (DMP-3000, DAEYOMEDI Co., Korea). And then we analyzed various parameters for sclerosis of the arteries such as Height (h1, h2, h3, h4, h5), Time (t1, t2, t3, t4, t5), AW, AW rate, Total area of pulse (At) and Augmentation Index (AIx). Results : As a result of analyzing parameters according to the aging, h2, h3, AS (systolic area rate to AT), AIx and AW were increased but t2/t, t3/t, t5/t and AD (diastolic area rate to AT) were decreased. Conclusions : We checked blood vessel conditions for normotensive group according to aging and confirmed various parameters. Also, we found that AW was analogous to AIx which has been used for diagnosing arteriosclerosis. Furthermore, we confirmed the usefulness of AW as a new parameter for checking vessel condition and characteristic compared with the AIx.

• 대한의용생체공학회:의공학회지
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• 제40권2호
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.353-357
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• 1997

We have developed a prototype of a module-based patient monitor. In this paper, we describe the design methodology and specifications of the developed module-based patient monitors. The monitor consists of a main unit and module cases with various parameter modules. The main unit includes a 12.1" TFT color LCD, a main CPU board, and peripherals such as a module controller, Ethernet LAN card, video card, rotate/push button controller, etc. The main unit can connect at maximum three module cases, each of which can accommodate up to 7 parameter modules. They include the modules for electrocardiograph, respiration, invasive blood pressure, noninvasive blood pressure, temperature, and $SpO_2$ with plethysmograph.

• 한국정보전자통신기술학회논문지
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• 제14권5호
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• pp.413-420
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• 2021

본 연구는 수집 파라미터 중 하나가 부족할 경우 다른 파라미터를 통해 부족한 파라미터를 예측하기 위한 기계학습 모델에 대한 연구로서, 실내 환경 데이터 수집 장치를 통해 시간에 따른 온도·습도·CO2농도·광량에 대한 데이터를 수집하고, 수집한 데이터를 Matlab내 기계학습 회귀분석 기능을 통해 시간·온도·습도·CO2·광량 데이터를 예측하는 회귀모델을 만들었다. 또한 각 파라미터별로 RMSE 값이 가장 적은 3가지 모델을 선정하였으며 이에 대한 검증을 진행했다. 검증을 위해 각 파라미터로 도출된 예측모델에 테스트 데이터를 적용하여 예측치를 구했으며, 실측치와 구해진 예측치 간의 상관계수와 오차 평균을 구한 후 이를 비교하였다.