• Asian-Australasian Journal of Animal Sciences
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• v.29 no.2
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• pp.299-306
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• 2016

Body temperature (BT) monitoring in cattle could be used to early detect fever from infectious disease or physiological events. Various ways to measure BT have been applied at different locations on cattle including rectum, reticulum, milk, subcutis and ear canal. In other to evaluate the temperature stability and reliability of subcutaneous temperature (ST) in highly fluctuating field conditions for continuous BT monitoring, long term ST profiles were collected and analyzed from cattle in autumn/winter and summer season by surgically implanted thermo-logger devices. Purposes of this study were to assess ST in the field condition as a reference BT and to determine any location effect of implantation on ST profile. In results, ST profile in cattle showed a clear circadian rhythm with daily lowest at 05:00 to 07:00 AM and highest around midnight and rather stable temperature readings (mean${\pm}$standard deviation [SD], $37.1^{\circ}C$ to $37.36^{\circ}C{\pm}0.91^{\circ}C$ to $1.02^{\circ}C$). STs are $1.39^{\circ}C$ to $1.65^{\circ}C$ lower than the rectal temperature and sometimes showed an irregular temperature drop below the normal physiologic one: 19.4% or 36.4% of 54,192 readings were below $36.5^{\circ}C$ or $37^{\circ}C$, respectively. Thus, for BT monitoring purposes in a fever-alarming-system, a correction algorithm is necessary to remove the influences of ambient temperature and animal resting behavior especially in winter time. One way to do this is simply discard outlier readings below $36.5^{\circ}C$ or $37^{\circ}C$ resulting in a much improved mean${\pm}$SD of $37.6^{\circ}C{\pm}0.64^{\circ}C$ or $37.8^{\circ}C{\pm}0.55^{\circ}C$, respectively. For location the upper scapula region seems the most reliable and convenient site for implantation of a thermo-sensor tag in terms of relatively low influence by ambient temperature and easy insertion compared to lower scapula or lateral neck.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.261-270
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• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of Acupuncture Research
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• v.30 no.4
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• pp.25-33
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• 2013

Objectives : To assess the test-retest reliability and the intratest repeatability in measuring the cervical range of motion of healthy subjects with wireless microelectromechanical system inertial measurement unit(MEMS-IMU) system and to discuss the feasibility of this system in the clinical setting to evaluate the cervical spine musculoskeletal. Methods : 12 healthy people who were evaluated as no- or mild-disability with neck disability index were participated. Their cervical motion were measured with IMU twice in consecutive two days for the test-retest reliability study. Intratest repeatability was calculated in the two tests separately. The calculated intraclass correlation coefficients(ICC) were discussed and compared with the those of the previous studies. Results : Cervical range of motion data were acquired and statistically processed: left rotation($61.64^{\circ}$), right rotation($65.12^{\circ}$), extension($61.98^{\circ}$), flexion($52.81^{\circ}$), left bending($39.31^{\circ}$), right bending($41.08^{\circ}$). ICCs were 0.77~0.98(intratest repeatability) and 0.74~0.93 (test-retest reliability) in the primary motion. In the coupling motion, intratest repeatability ICCs were 0.93~ 0.99(transverse primary plane), 0.88~0.97(saggital primay plane), and 0.77~0.93(coronal primary plane). Test-retest reliability of coupling motion were 0.90~0.97(transverse primary plane), 0.00~0.72(saggital primary plane), and 0.04~0.76(coronal primary plane). Conclusions : Several types of range-of-motion devices are now on use in many fields including medicine, but the practicality of the devices in clinical use is questionable for the convenient and economical aspects. In this study, we presented the reliability of cervical range of motion test with the developed wireless MEMS-IMU system and discussed its potential utility in clinical use.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1265-1274
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• 2021

Two field experiments were conducted to improve the conception rate of Hanwoo cow. The first experiment aimed to investigate the physiological condition of Hanwoo cows on estrus, including metabolic profiles and body condition score (BCS). The second experiment investigated the effect of a novel estrus detector on the artificial insemination (AI) conception rate for Hanwoo cows. For the first experiment, 80 Hanwoo cows (2.5 ± 0.10 of parity), approximately one month before estrus, were housed in 16 pens and offered the experimental diets twice daily with free water access. The BCS were recorded, and blood was collected from the jugular veins just before AI. The collected blood was used to measure physiological conditions, such as metabolite and hormone levels. For the second experiment, each cow was equipped with a neck-mounted estrus detector collar, which had a sensor connected through the internet. Approximately one month before estrus, three hundred sixty Hanwoo cows (2.4 ± 0.21 of parity) were assigned into groups with or without W-Tag collar treatments. The animals were managed the same as in the first experiment. The pregnancy rate reached 55% in the first experiment. The concentration of luteinizing hormone (LH) was higher (p < 0.012; 1.56 vs. 1.08 ng/mL) in cows that were not pregnant (NPG) than in cows that were pregnant (PG) after AI. The BCS and other concentrations of metabolites and hormones in the blood were not different in both NPG and PG cows. The ranges of estrogen, LH, and follicle-stimulating hormone for PG cows were 11.9 to 39.0 pg/mL, < 0.25 to 1.98 ng/mL, and < 0.50 to 0.82 ng/mL, respectively. In the second experiment, cows with the estrus detector had lower days open (p < 0.001; 78.1 vs. 84.8 d), insemination frequency (p < 0.001; 1.26 vs. 2.52), and return of estrus (p < 0.001; 70.9 vs. 79.1 d) than those in cows without the estrus detector. In conclusion, the present study indicated that lower LH concentration just before AI potentially increased the pregnancy rate of Hanwoo cows. Furthermore, the application of estrus detectors to Hanwoo cows could improve the conception success rate for AI.

• Korean Journal of Clinical Laboratory Science
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• v.44 no.2
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• pp.52-58
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• 2012

Patients with obstructive sleep apnea (OSA) often have more aggravated symptoms in the supine position. We tried to investigate the clinical characteristics and the predictive factors for positional OSA. Polysomnographic data were reviewed for OSA patients (apnea hypopnea index, $AHI{\geq}5$) from April, 2008 to April, 2011 at the Asan Medical Center. Clinical data, comorbid medical condition data and questionnaires (SF-36, MFI-20, ESS, BDI, STAI) were assessed. All patients were classified into two groups: positional patients (PP) group and non-positional patients (NPP) group. PP was defined as a patient who had the AHI in the supine position was at least twice as high as that in the lateral position. The body position of patients was confirmed by sleep position sensor and video monitor. All patients had at least 30 minutes of positional and 30 minutes of non-positional sleep. We compared clinical, medical, polysomnographic data, and questionnaire results between two (PP and NPP) groups and investigated predictive factors for the PP group using binary logistic regression analysis. In total, 371 patients were investigated. 265 (71.4%) was categorized as PP group and 106 (28.5%) as NPP group. The mean age ($mean{\pm}SD$) was higher in the PP group ($52.4{\pm}9.8$) than in the NPP group ($49.5{\pm}11.9$) (p<0.05). Comparison of sleep parameters between the PP and the NPP group showed that the PP group had significantly lower BMI (PP: $26.1{\pm}3.2kg/m^2$; NPP: $27.8{\pm}4.3kg/m^2$, p<0.001), neck circumference (PP: $39.7{\pm}2.8cm$; NPP: $41.5{\pm}3.7cm$, p<0.001) and hypertension rate (PP: n=89/265 (33.5%); NPP: n=48/106 (45.2%), p=0.0240). In the PP group, the percentage of deep sleep (PP: $8.7{\pm}8.1%$; NPP: $5.6{\pm}7.0%$, P=0.001) and rapid eye movement (REM) (PP: $17.5{\pm}6.1%$; NPP: $14.0{\pm}6.9%$, p<0.001) were significantly higher whereas the percentage of light sleep (stage N1) was significantly lower than the NPP group (PP: $30.4{\pm}12.3$; NPP: $44.5{\pm}20.8%$, p<0.001). During the sleep, the AHI in the supine position (PP: $48.6{\pm}19.5$; NPP: $60.5{\pm}22.6$, p<0.001) and in the non-supine position (PP: $9.4{\pm}8.9$; NPP: $48.4{\pm}24.8$, p=<0.001) were significantly lower and the minimal arterial oxygen saturation in non-REM sleep was significantly higher in the PP group (PP: $80.3{\pm}7.6$; NPP: $75.1{\pm}9.9$, p=<0.001). There were no significant differences in all questionnaires including quality of life. The results of the binary logistic regression analysis showed that age, the amount of REM sleep(%) and AHI were significant predictive factors for positional OSA. The significant predictive factors for positional OSA were older age, higher percentage of REM and lower AHI. The questionnaire results were not significantly different between the two groups.