• Journal of Korean Academy of Nursing
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• v.31 no.5
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• pp.793-806
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• 2001

To identify nursing interventions performed by Chosunjok nurses in Yanbian using NIC. Methods: The sample consisted of 36 nurses working in 2 hospitals. The Nursing Interventions Use Questionnaire developed by the Iowa Intervention Project team was used for data collection. The instrument was translated to Korean using the method of back-translation. Results: Twenty-eight interventions were performed at least daily. Interventions in the Physiological: Basic domain were most frequently used at least daily. The most frequently used interventions was Positioning, followed by the interventions Pressure Ulcer Prevention, Intravenous (IV) Therapy, Hypothermia Treatment and Intravenous (IV) Insertion. The least frequently used interventions was Electronic Fetal Monitoring: Antepartum. Nurses working in special medical care units performed interventions most often, while nurses working in general surgical units performed them least. Nurses working in general medical, special medical and other care units performed interventions in the Physiological domain more often than the nurses working in general surgical units. Conclusion: Chosunjok nurses in Yanbian performed physiological interventions frequently. Further studies will be needed to compare interventions performed by nurses in two countries.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.579-587
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• 2023

In this study, rumen temperature and environment in estral and non-estral Korean Native breeding cattle were evaluated by using a bolus sensor. Behavioral and physiological changes in study animals were also assessed. To assess the rumen temperature and environment, we inserted bolus sensors into 12 Korean Native cattle with an average age of 35.5 months, then measured temperature and activity within the rumen using the wireless bolus sensor. Drinking, feeding and mounting behavior, and measured vaginal temperature and levels of intravaginal mucus resistance were recorded. We found that cattle in estrus exhibited more acts of mounting (37.4 vs. 0 times/day), increased vaginal temperature (39.0℃ vs. 38.4℃), and decreased vaginal mucus resistance (136.3 Ω vs 197.4 Ω), compared with non-estral animals. Furthermore, increased levels of rumen activity were most significant in estrus cattle at the highest activity levels (p < 0.01). Overall, the estrus group exhibited increased rumen temperature (p = 0.01), compared with the non-estrus group. In conclusion, the results of this study not only provide basic physiological data related to estrus in improved Korean Native breeding cattle, but also suggest that monitoring of rumen temperature and activity might be used as an effective smart device for estrus detection.

• Journal of Biomedical Engineering Research
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• v.14 no.1
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• pp.81-88
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• 1993

This paper is the study of the reflectance of light from biological tissue for red and Infrared wavelengths and relates the acquired reflectance data to expected physiological changes within the skin and muscle layers associated with heat and exercise. The instrument was disigned to collect data from the calf muscle in human subjects with probe located at the surface of skin. Rapid data acquisition method allowed monitoring of rapid changes in reflecttance due to a stimulus. This study demonstrates that changes in O2 saturation and blood fractional volume expected within the dermis and muscle layers were asserted by examining the slopes of the plotted index for heat and exercise. The results presented in thls study support the claim that reflectance can separately discriminate between changes of blood volume and oxygenation in muscle and in skin. The data demonstrate the ability to measure consistent changes In tissue optical properties during exercise and heat.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.113-116
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• 2000

In this paper presents the portable wireless ECG data detection and diagnosis system based on discreet wavelet transform. An algorithm based on wavelet transform suitable for real time implementation has been developed in order to detect ECG characteristics. In particular, QRS complex, S and T waves may be distinguished form noise, baseline drift or artifacts. Proposed telemetry system that a transmitting media using radio frequency(RF) for the middle range measurement of the physiological signals and receiving media using optical for electromagnetic interference problem. A standard hi-directional serial communication interface between the telemetry system and a personal computer or laptop, allows read-time controlling, diagnosing and monitoring of system. A portable telemetry system within a size. of 65${\times}$125${\times}$45mm consists of three parts: a digital signal processing part for physiological signal detect or diagnose, RF transmitter for data transfer and a optical receiver for command receive. Advantages of proposed telemetry system is wireless middle range(50m) FM transmission, reduce electromagnetic interference to a minimum. which enables a comfortable diagnosis system at home.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

• Korean Journal of Psychosomatic Medicine
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• v.4 no.1
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• pp.85-90
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• 1996

Cognitive-behavioral approach to two cases with chronic headache was presented. Cognitive-behavioral interventions focus on indirectly altering symptom-related physiological activity by changing the way patients cope with headache-eliciting stressors. This treatment focuses directly on the patients' cognitive and behavioral changes. Cognitive-behavioral treatment can be divided into three phases Education, self-monitoring, and problem-solving or coping-skills training. Literature reviews on the follow-up evaluation of therapeutic effectiveness revealed that cognitive-behavioral treatment is effective in the management of chronic headache.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.493-496
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• 2005

For home healthcare, the unconstrained measurement of physiological signal is highly required to avoid the inconvenience of users. The recording and analysis of the fundamental parameters during sleep like respiration and heart beat provide valuable information on his/her healthcare. Using the air mattress sensor system, the respiration and heart beat movements can be measured without any harness or sensor on the subject's body. The differential measurement technique between two air cells is adopted to enhance the sensitivity. The balancing tube between two air cells is used to increase the robustness against postural changes during the measurement period. The meaningful frequency range could be selected by the pneumatic filter with balancing tube. ECG (Electrocardiography) and respiration sensor (plethysmography) were measured for comparison with the signal from air mattress. To extract the heart beat information from air pressure sensor, digital signal processing technique was used. The accuracy for breathing interval and heart beat monitoring was acceptable. It shows the potentials of air mattress sensor system to be the unconstrained home sleep monitoring system.

• Ocean Science Journal
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• v.42 no.1
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• pp.49-59
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• 2007

Besides empirical algorithms with the blue-green ratio, the algorithms based on fluorescence are also important and valid methods for retrieving chlorophyll-a concentration in the ocean waters, especially for Case II waters and the sea with algal blooming. This study reviews the history of initial cognitions, investigations and detailed approaches towards chlorophyll fluorescence, and then introduces the biological mechanism of fluorescence remote sensing and main spectral characteristics such as the positive correlation between fluorescence and chlorophyll concentration, the red shift phenomena. Meanwhile, there exist many influence factors that increase complexity of fluorescence remote sensing, such as fluorescence quantum yield, physiological status of various algae, substances with related optical property in the ocean, atmospheric absorption etc. Based on these cognitions, scientists have found two ways to calculate the amount of fluorescence detected by ocean color sensors: fluorescence line height and reflectance ratio. These two ways are currently the foundation for retrieval of chlorophyll-a concentration in the ocean. As the in-situ measurements and synchronous satellite data are continuously being accumulated, the fluorescence remote sensing of chlorophyll-a concentration in Case II waters should be recognized more thoroughly and new algorithms could be expected.

• Journal of Computing Science and Engineering
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• v.10 no.3
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• pp.75-84
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• 2016

Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.