• Journal of International Academy of Physical Therapy Research
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• v.11 no.1
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• pp.1940-1949
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• 2020

Background: Self-checked monitoring home exercises are recommended for preventing falls among people with Parkinson's disease. However, as these home exercises are performed autonomously by patients without professional management, their accuracy and efficiency can be compromised. Objective: To investigate the effects of providing regular training sessions to patients and caregivers and of patient self-monitoring of exercise performance following the implementation of a self-checked monitoring exercise program for people with Parkinson's disease. Design: Randomized Pretest-Posttest Control Group Design. Methods: We provided regular self-checked monitoring home exercise and general home exercise programs to 30 participants for 12 weeks. Once a month at the first, fifth, and ninth-week sessions, a rehabilitation team attended the Parkinson's group education. In addition to the subject in the experimental group perform the home exercises program to provide feedback regarding the home exercises program and to carry out a self-monitoring checklist performance for 12 weeks. Results: The 10 m walk test, functional reach test, and sit to stand test and the modified Barthel index significantly improved in the self-checked monitoring home exercise group. Conclusion: These results suggest that self-checked home exercise programs, which facilitate safety and consistent performance of exercises at home, are beneficial for people with Parkinson's disease.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.41.1-41.12
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• 2020

Background: The poultry red mite, Dermanyssus gallinae, is a serious problem in the laying hen industry worldwide. Currently, the foremost control method for D. gallinae is the implementation of integrated pest management, the effective application of which necessitates a precise monitoring method. Objectives: The aim of the study was to propose an accurate monitoring method with a reliable protocol for caged-layer poultry farms, and to suggest an objective classification for assessing D. gallinae infestation on caged-layer poultry farms according to the number of mites collected using the developed monitoring method. Methods: We compared the numbers of mites collected from corrugated cardboard traps, regarding with length of sampling periods, sampling sites on cage, and sampling positions in farm buildings. The study also compared the mean numbers of mites collected by the developed method with the infestation levels using by the conventional monitoring methods in 37 caged-layer farm buildings. Results: The statistical validation provided the suitable monitoring method that the traps were installed for 2 days on feed boxes at 27 sampling points which included three vertical levels across nine equally divided zones of farms. Using this monitoring method, the D. gallinae infestation level can be assessed objectively on caged-layer poultry farms. Moreover, the method is more sensitive than the conventional method in detecting very small populations of mites. Conclusions: This method can be used to identify the initial stages of D. gallinae infestation in the caged-layer poultry farms, and therefore, will contribute to establishment of effective control strategies for this mite.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1095-1105
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• 2013

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

• Journal of Veterinary Clinics
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• v.16 no.2
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• pp.428-438
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• 1999

A study was conducted to estimate cost of major dairy cattle diseases. Forty (n=40) of the 167 dairy herds in Gyeongnam (Chinju) area were stratified and selected randomly for participation in the national animal health monitoring system. Gyeongsnag University veterinarians, Gyeongnam Livestock Promotion Institute veterinarians and clinic veterinarian visited each herd once a month for a total periods of 12 months. At a each visit data on disease, production, management, finance, treatments, preventive activities, animal events, and any other relevant events were collected. Monthly and annual cost estimates of disease treatment were in computed in each herd and stratum(including cost of prevention). Results were expressed as cost per head and given separately for cows, young stock, and calves. In cows, the most expensive seven diseases entities (from the most to the least) were : (1) clinical mastitis; (2) breeding problems; (3)gastrointestinal problems; (4) multiple system problem; (5) birth problems; (6) metabolic/nutritional disease; (7) lameness. In young stock, the most costly disease were the multiple system problems, breeding problems, respiratory disease, gastrointestinal disease, and lameness. In calves, the most costly disease problems were gastrointestinal problems, respiratory disease, integumental, multiple system problems, and metabolic/nutritional problems.

• IE interfaces
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• v.23 no.4
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• pp.365-371
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• 2010

In this study, we propose a novel remote health monitoring system to accurately predict Parkinson's disease severity using a signomial regression method. In order to characterize the Parkinson's disease severity, sixteen biomedical voice measurements associated with symptoms of the Parkinson's disease, are used to develop the telemonitoring model for early detection of the Parkinson's disease. The proposed approach could be utilized for not only prediction purposes, but also interpretation purposes in practice, providing an explicit description of the resulting function in the original input space. Compared to the accuracy performance with the existing methods, the proposed algorithm produces less error rate for predicting Parkinson's disease severity.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.3
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• pp.5-8
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• 2004

• Clinical Endoscopy
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• v.55 no.4
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• pp.480-488
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• 2022

Endoscopy is vital for diagnosis, assessing treatment response, monitoring and surveillance in patients with inflammatory bowel disease (IBD). With the growing importance of mucosal healing as a treatment target, the assessment of disease activity by endoscopy has been accepted as the standard of care for IBD. There are many endoscopic activity indices for facilitating standardized reporting of the gastrointestinal mucosal appearance in IBD, and each index has its strengths and weaknesses. Although most endoscopic indices do not have a clear-cut validated definition, endoscopic remission or mucosal healing is associated with favorable outcomes, such as a decreased risk of relapse. Therefore, experts suggest utilizing endoscopic indices for monitoring disease activity and optimizing treatment to achieve remission. However, the regular monitoring of endoscopic activity is limited in practice owing to several factors, such as the complexity of the procedure, time consumption, inter-observer variability, and lack of a clear-cut, validated definition of endoscopic response or remission. Although experts have recently suggested consensus-based definitions, further studies are needed to define the values that can predict long-term outcomes.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.2.1-2.18
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• 2022

Background: Co-infections of the porcine reproductive and respiratory syndrome virus (PRRSV) and the Haemophilus parasuis (HPS) are severe in Chinese pigs, but the immune response genes against co-infected with 2 pathogens in the lungs have not been reported. Objectives: To understand the effect of PRRSV and/or HPS infection on the genes expression associated with lung immune function. Methods: The expression of the immune-related genes was analyzed using RNA-sequencing and bioinformatics. Differentially expressed genes (DEGs) were detected and identified by quantitative real-time polymerase chain reaction (qRT-PCR), immunohistochemistry (IHC) and western blotting assays. Results: All experimental pigs showed clinical symptoms and lung lesions. RNA-seq analysis showed that 922 DEGs in co-challenged pigs were more than in the HPS group (709 DEGs) and the PRRSV group (676 DEGs). Eleven DEGs validated by qRT-PCR were consistent with the RNA sequencing results. Eleven common Kyoto Encyclopedia of Genes and Genomes pathways related to infection and immune were found in single-infected and co-challenged pigs, including autophagy, cytokine-cytokine receptor interaction, and antigen processing and presentation, involving different DEGs. A model of immune response to infection with PRRSV and HPS was predicted among the DEGs in the co-challenged pigs. Dual oxidase 1 (DUOX1) and interleukin-21 (IL21) were detected by IHC and western blot and showed significant differences between the co-challenged pigs and the controls. Conclusions: These findings elucidated the transcriptome changes in the lungs after PRRSV and/or HPS infections, providing ideas for further study to inhibit ROS production and promote pulmonary fibrosis caused by co-challenging with PRRSV and HPS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.440-446
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• 2019

Pine wilt disease occurred for the first time in Busan in 1988 and the damage has since been increasing. In 2005, a special law was enacted for pine wilt disease by Korea Forest Service. Incidences relating to the forest pest had been frequent and chemical control as well as physical control techniques had been applied to control it. Therefore, there is a need to reduce the damage caused by the pine wilt disease through intensive management such as continuous monitoring, control, and monitoring based on active control as well as management measures. In this study, the UAV-based monitoring method was proposed as an economical way of monitoring the forest pest. The efficiency of the existing method and UAV method had been analyzed, and as a result the study suggested that UAV can be used for forest pest monitoring and indeed improve efficiency. The UAV-based forest pest monitoring method has a cost reduction of about 50% compared with the conventional method and will also help to reduce the area where the survey was omitted.

• 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.