• 국제물리치료학회지
• /
• 제11권1호
• /
• pp.1940-1949
• /
• 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
• /
• 제21권3호
• /
• pp.41.1-41.12
• /
• 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.

• 한국통신학회논문지
• /
• 제38C권12호
• /
• pp.1095-1105
• /
• 2013

본 논문에서는 헬스 케어 시스템에서 효율적으로 질병을 관리할 수 있는 RDMS(Rough Set Theory based Disease Monitoring System)을 제안한다. RDMS는 DCM(Data Collection Module), RDRGM(RST based Disease Rule Generation Module), HMM(Healthcare Monitoring Module)로 구성된다. DCM은 바이오센서로부터 환자의 생체 정보를 수집하고, 데이터 처리 절차에 따라 RDMS DB에 저장한다. RDRGM은 RST의 코어와 속성의 지지율을 이용하여 질병 규칙을 생성한다. HMM은 DCM에 의해 수집된 환자 정보를 이용하여 환자의 질병에 대한 위험지수뿐만 아니라 질병에 대한 합병증에 관한 위험지수까지 분석함으로써 환자의 질병을 예측하고, 환자의 위험지수에 따라 환자, 주치의 등에 시각화된 환자의 정보를 전달한다. 또한, RDRGM에 의해 생성된 규칙들에 따라 환자의 의료정보, 현재의 환자건강상태, 환자 가족력 등을 비교분석하여 환자의 질병을 예측하고, 예측결과에 따라 환자 맞춤형 의료 서비스와 의료 정보를 신속하고 신뢰성 있게 제공할 수 있다.

• 한국임상수의학회지
• /
• 제16권2호
• /
• pp.428-438
• /
• 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.

• 산업공학
• /
• 제23권4호
• /
• pp.365-371
• /
• 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.

• 한국정밀공학회지
• /
• 제21권3호
• /
• pp.5-8
• /
• 2004

• Clinical Endoscopy
• /
• 제55권4호
• /
• pp.480-488
• /
• 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
• /
• 제23권1호
• /
• pp.2.1-2.18
• /
• 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.

• 한국산학기술학회논문지
• /
• 제20권1호
• /
• pp.440-446
• /
• 2019

우리나라는 1988년 부산에서 처음 소나무 재선충병이 발생하였으며, 산림청은 국내 소나무림의 보호 및 산림자원의 확보를 위해 2005년에 소나무 재선충병에 대한 특별법을 제정하였다. 현재 소나무 재선충병의 발생 빈도는 증가추세를 보이고 있으며, 발생지역 확대를 막기 위해 화학적 통제 및 물리적 통제 기술이 적용되고 있다. 소나무 재선충병의 방제를 위해서는 먼저 피해 상황을 파악하고 주변 환경 및 특성을 고려한 최적의 방제 계획 수립이 필요하며, 넓은 지역에 대한 모니터링 방안으로 최근 UAV(Unmanned Aerial Vehicle)에 대한 관심이 증가하고 있다. 이에 본 연구에서는 UAV 기반 산림 모니터링 방법에 대한 경제성을 평가하고자 하였다. 기존 인력에 의한 모니터링 방법과 UAV를 적용한 방법의 효율성을 분석 한 결과, UAV 기반 산림 병해충 감시 방법은 기존 방법에 비해 약 50%의 비용 절감 효과가 있으며, 산림 조사의 누락 지역을 줄이는데도 도움이 될 것이며, 향후 추가적인 연구를 통해 UAV 기반의 산림 모니터링 방안의 검증이 이루어진다면 산림 조사 관련업무의 효율성이 크게 증가할 것이다.

• Journal of Animal Science and Technology
• /
• 제66권1호
• /
• pp.31-56
• /
• 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.