Support Vector Machine Analysis to Detect Deviation in a Health Condition Monitoring System

In this study, support vector machine (SVM) learning was applied to a proposed monitoring system that captures changes in a person’s health conditions using flexible force-sensing resistors and optimizing parameters. The system consists of eight flexible force-sensing resistors, a data acquisition device and a personal computer. Feature quantities were defined using the time difference between the output signal from sensor 1 which specifies the initiation of the measurement and that from other sensors. The measurement conditions were the normal range of motion, simulated limited shoulder and knee joint. The measurement data were divided into 30 sets for learning data and 15 sets for test data. The SVM module was used for analysis. Comparing the difference between the linear function kernel and radial basis function kernel, there was no major difference based on learning data. However, an 83 % accuracy rate was observed using the radial basis function kernel. For test data, the highest accuracy was obtained when t2 and t7 were used as the feature quantities

The Measurement of Blood Coagulation Process in Extracorporeal Circuit Using LED Photoacoustic Imaging

Blood coagulation is measured by using a pressure sensor in a blood circuit, but it is not quick responsive because it is detected by pressure rise caused by coagulation. In this study, we have investigated a method to detect blood clotting at an early stage using photoacoustic imaging, which is thought to be more sensitive. The LED with a wavelength of 850 nm was used as a photoacoustic light source. An ultrasonic wave generated by thermal expansion of mouse blood sealed in a microtube was observed, and also many ripples were observed with time and the coagulation of blood progressed. It was also observed that the waveform considered to correspond to coagulation of blood broadens with time. It was found from the above that there is a possibility that the state of blood clotting can be observed from outside the circuit of the extracorporeal circulation device by using the LED as a light source

Feature Value Classification Based on the Position Difference ​of Pressure Sensors Installed in Insoles and Their Outputs

The current interest in quantum technologies calls for the development of novel materials and hybrid structures. Understanding the mechanical properties of a material can be a challenge, especially at the nanoscale. We use the eigenfrequencies of in-house fabricated silicon nitride membranes in combination with finite-element simulations to extract the stress in a film that is deposited on top. The high stress results in sharp resonances that can be located precisely so that the mechanical properties of the top layer can be determined accurately. We highlight this approach using aluminum nitride – an important material for on-chip quantum optics and optomechanics – grown onto these micromechanical membranes. The detection is done optomechanically by exciting the modes using a piezo actuation and detecting the vibrations in the reflected laser light. For this, different lasers are at our disposal. The resonances of a wide variety of highly stressed membranes are measured. The frequencies follow the expected inverse length dependence of a stressed membrane and depend on the thickness of the top layer. To connect the experimental observations to the material properties, finite-element simulations are used. It is shown that full simulations of the membranes are only possible for simplified geometries. When simulating the actual geometry, this, however, becomes infeasible. It is shown that simulations of a single unit cell – in particular band structure calculations – can be used to accurately model the actual structure of the membrane. Although this approach is strictly speaking only valid for infinitely large membranes, it is shown that edge effects are negligible. With the simulations, the stress in the bilayer is determined. A cross-over between compressive and tensile stress is observed as a function of the AlN thickness

Exploring the Assessment of Steps Using Insoles ​with Four-part Pressure Sensors

This study focused on the potential use of smart insoles incorporating four-part pressure sensors in the field of rehabilitation. The assessment of walking not only addresses walking issues but also contributes to understanding a person’s health condition because the health condition manifests in the manner a person walk. With the aim of assessing the influence of activity on the body using digital devices, we investigated walking steps before and after using a walking rehabilitation robot using a wireless smart insole with four pressure sensors on each side and an accelerometer attached to the shoe. This study explored the disparities in the arrangement of pressure sensor data integrated into the insole, differences resulting from analysis methods, and their relationship with the accelerometer were investigated. The analysis results for the walking steps before robot attachment, during robot-assisted walking, and after robot attachment of distinctive features for each of the four parts of the insole demonstrate the potential of the pressure-sensor-equipped smart insole in detecting changes in walking patterns

Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits

In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware

Reliability and Validity of the Japanese Version of the Basel Assessment of Adherence to Immunosuppressive Medications Scale in Kidney Transplant Recipients

A valid and reliable instrument that can measure adherence is needed to identify nonadherent patients and to improve adherence. However, there is no validated Japanese self-report instrument to evaluate adherence to immunosuppressive medications for transplant patients. The purpose of this study was to determine the reliability and validity of the Japanese version of the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS).; We translated the BAASIS into Japanese and developed the Japanese version of the BAASIS (J-BAASIS) according to the International Society of Pharmacoeconomics and Outcomes Research task force guidelines. We analyzed the reliability (test-retest reliability and measurement error) and validity of the J-BAASIS (concurrent validity with the medication event monitoring system and the 12-item Medication Adherence Scale) referring to the COSMIN Risk of Bias checklist.; A total of 106 kidney transplant recipients were included in this study. In the analysis of test-retest reliability, Cohen's kappa coefficient was found to be 0.62. In the analysis of measurement error, the positive and negative agreement were 0.78 and 0.84, respectively. In the analysis of concurrent validity with the medication event monitoring system, sensitivity and specificity were 0.84 and 0.90, respectively. In the analysis of concurrent validity with the 12-item Medication Adherence Scale, the point-biserial correlation coefficient for the "medication compliance" subscale was 0.38 (; P; < 0.001).; The J-BAASIS was determined to have good reliability and validity. Using the J-BAASIS to evaluate adherence can help clinicians to identify medication nonadherence and institute appropriate corrective measures to improve transplant outcomes

The YUIMA project : a computational framework for simulation and inference of stochastic differential equations

The Yuima Project is an open source and collaborative e ort aimed at developing the R package named yuima for simulation and inference of stochastic di erential equations. In the yuima package, stochastic di erential equations can be of very abstract type, multidimensional, driven by Wiener process or fractional Brownian motion with general Hurst parameter, with or without jumps speci ed as L evy noise. The yuima package is intended to o er the basic infrastructure on which complex models and inference procedures can be built on. The computational framework implemented allow for the estimation of high frequency data and also o er the ability to perform Monte Carlo anal- ysis using cluster infrastructure whenever available in a transparent way to the user. Some real examples of model implementation and data estimation will be considere

Autobullectomy with COVID-19 in a patient with chronic obstructive pulmonary disease

application/pdfA 72-year-old man with chronic obstructive pulmonary disease (COPD) was admitted for coronavirus disease 2019 (COVID-19). He was discharged on day 30; however, he was readmitted 6 days later due to a left lung organizing pneumonia secondary to COVID-19. After methylprednisolone treatment, the patient was discharged on day 15. One year later, computed tomography showed shrinkage of emphysematous lesions, and both total lung capacity measured using computed tomography and fraction of low attenuation volume decreased in the left lung compared to that before COVID-19. Here, we report a rare case of autobullectomy with COVID-19 in a patient with COPD.Journal Articlejournal articl

Multicenter, single-blind, randomized controlled study of the efficacy and safety of favipiravir and nafamostat mesilate in patients with COVID-19 pneumonia

Objectives: To evaluate the efficacy and safety of nafamostat combined with favipiravir for the treatment of COVID-19. Methods: We conducted a multicenter, randomized, single-blind, placebo-controlled, parallel assignment study in hospitalized patients with mild-to-moderate COVID-19 pneumonia. Patients were randomly assigned to receive favipiravir alone (n = 24) or nafamostat with favipiravir (n = 21). The outcomes included changes in the World Health Organization clinical progression scale score, time to improvement in body temperature, and improvement in oxygen saturation (SpO2). Results: There was no significant difference in the changes in the clinical progression scale between nafamostat with favipiravir and favipiravir alone groups (median, -0.444 vs -0.150, respectively; least-squares mean difference, -0.294; P = 0.364). The time to improvement in body temperature was significantly shorter in the combination group (5.0 days; 95% confidence interval, 4.0-7.0) than in the favipiravir group (9.0 days; 95% confidence interval, 7.0-18.0; P =0.009). The changes in SpO2 were greater in the combination group than in the favipiravir group (0.526% vs -1.304%, respectively; least-squares mean difference, 1.831; P = 0.022). No serious adverse events or deaths were reported, but phlebitis occurred in 57.1% of the patients in the combination group. Conclusion: Although our study showed no differences in clinical progression, earlier defervescence, and recovery of SpO2 were observed in the combination group