A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn/

A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment

A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment

Application of the Korean version of the Modified Barthel Index: Development of a keyform for use in clinical practice

Objective: To demonstrate the clinical application of the Korean version of the Modified Barthel Index (K-MBI) using Rasch analysis. Methods: A total of 276 patients with neurological disorders were assessed with the K-MBI in outpatient clinics. The Rasch partial-credit model was used to generate a keyform based on investigating the psychometric properties of the K-MBI, including dimensionality, precision (person strata and reliability), and hierarchical item difficulty. The Minimal Detectable Change (MDC) in item difficulty was used to establish right-challenging treatment goals and long-term treatment plans. Results: The findings demonstrated that the Korean version of the MBI satisfied the assumption of unidimensionality. It also showed a hierarchical structure in terms of item difficulty, good reliability (Cronbach alpha, 0.92), and approximately five distinct person strata (4.6). The MDC (raw score, 20.1) of the item difficulty of the test items demonstrated equivalent cut-off scores for targeted short-term treatment goals on the keyform, a Rasch-derived display of patient responses. Long-term treatment goals were identified based on the test items of the keyform. Conclusions: The findings suggest that a Rasch keyform can be applied to clinical practice in Korean settings by identifying clinically and statistically meaningful test items and their step thresholds as short- and long-term goals

Decreasing Incidence and Mortality in Traumatic Brain Injury in Korea, 2008–2017: A Population-Based Longitudinal Study

Traumatic brain injury (TBI), a global public health concern, may lead to death and major disability. While various short-term, small-sample, and cross-sectional studies on TBI have been conducted in South Korea, there is a lack of clarity on the nationwide longitudinal TBI trends in the country. This retrospective study investigated the epidemiological TBI trends in South Korea, using a population-based dataset of the National Health Insurance (2008–2017). The crude and age adjusted TBI incidence and mortality values were calculated and stratified by age, sex, and TBI diagnosis. The age-adjusted incidence per 100,000 people increased until 2010 and showed a decreasing trend (475.8 cases in 2017) thereafter; however, a continuously decreasing age-adjusted mortality trend was observed (42.9 cases in 2008, 11.3 in 2017). The crude incidence rate increased continually in those aged >70 years across all the TBI diagnostic categories. The mortality per 100,000 people was significantly higher among participants aged ≥70 years than in the other age groups. We observed changing trends in the TBI incidence, with a continuously decreasing overall incidence and a rapidly increasing incidence and high mortality values in older adults. Our findings highlight the importance of active TBI prevention in elderly people

The Relationship between 10-Year Cardiovascular Risk Calculated Using the Pooled Cohort Equation and the Severity of Non-Alcoholic Fatty Liver Disease

BackgroundWe investigated the association between the severity of non-alcoholic fatty liver disease (NAFLD) and the estimated 10-year risk of cardiovascular disease (CVD) calculated by Pooled Cohort Equation (PCE) and Framingham risk score (FRS).MethodsA total of 15,913 participants (mean age, 46.3 years) in a health screening program were selected for analysis. The presence and severity of fatty liver was assessed by abdominal ultrasonogram. Subjects who drank alcohol more than three times a week were excluded from the study.ResultsAmong the participants, 57.6% had no NAFLD, 35.4% had grade I, 6.5% had grade II, and 0.5% had grade III NAFLD. Mean estimated 10-year CVD risk was 2.59%, 3.93%, 4.68%, and 5.23% calculated using the PCE (P for trend <0.01) and 4.55%, 6.39%, 7.33%, and 7.13% calculated using FRS, according to NAFLD severity from none to severe (P for trend <0.01). The odds ratio for ≥7.5% estimated CVD risk calculated using the PCE showed a higher correlation with increasing severity of NAFLD even after adjustment for conventional CVD risk factors (1.52, 2.56, 3.35 vs. the no NAFLD group as a reference, P<0.01) compared with calculated risk using FRS (1.65, 1.62, 1.72 vs. no NAFLD group as a reference, P<0.01).ConclusionIn our study of apparently healthy Korean adults, increasing severity of NAFLD showed a higher correlation with estimated 10-year CVD risk when calculated using the PCE than when calculated using FRS

Metabolic Health Is More Important than Obesity in the Development of Nonalcoholic Fatty Liver Disease: A 4-Year Retrospective Study

BackgroundThe aim of this study is to compare the risk for future development of nonalcoholic fatty liver disease (NAFLD) according to different status of metabolic health and obesity.MethodsA total of 3,045 subjects without NAFLD and diabetes at baseline were followed for 4 years. Subjects were categorized into four groups according to the following baseline metabolic health and obesity statuses: metabolically healthy, non-obese (MHNO); metabolically healthy, obese (MHO); metabolically unhealthy, non-obese (MUHNO); and metabolically unhealthy, obese (MUHO). Being metabolically healthy was defined as having fewer than two of the following five components: high blood pressure, high fasting blood glucose, high triglyceride, low high density lipoprotein cholesterol, and being in the highest decile of the homeostasis model assessment-insulin resistance index. Obesity was defined as a body mass index >25 kg/m2. The presence of NAFLD was assessed by ultrasonography.ResultsThe proportions of subjects included in the MHNO, MHO, MUHNO, and MUHO groups were 71.4%, 9.8%, 13.0%, and 5.8%, respectively. The proportions of subjects who developed NAFLD were 10.5%, 31.4%, 23.2%, and 42% in the MHNO, MHO, MUHNO, and MUHO groups, respectively. The risk for developing NAFLD was highest in subjects who were metabolically unhealthy both at baseline and after 4 years compared with subjects who were consistently metabolically healthy during the follow-up period (odds ratio, 2.862). Using the MHNO group as reference, the odds ratios for the MHO, MUHNO, and MUHO groups were 1.731, 1.877, and 2.501, respectively.ConclusionThe risk for NAFLD was lower in MHO subjects than in MUNO subjects