Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot.

This study introduces a novel convolutional neural network (CNN) architecture, encompassing both single and multi-head designs, developed to identify a user's locomotion activity while using a wearable lower limb robot. Our research involved 500 healthy adult participants in an activities of daily living (ADL) space, conducted from 1 September to 30 November 2022. We collected prospective data to identify five locomotion activities (level ground walking, stair ascent/descent, and ramp ascent/descent) across three terrains: flat ground, staircase, and ramp. To evaluate the predictive capabilities of the proposed CNN architectures, we compared its performance with three other models: one CNN and two hybrid models (CNN-LSTM and LSTM-CNN). Experiments were conducted using multivariate signals of various types obtained from electromyograms (EMGs) and the wearable robot. Our results reveal that the deeper CNN architecture significantly surpasses the performance of the three competing models. The proposed model, leveraging encoder data such as hip angles and velocities, along with postural signals such as roll, pitch, and yaw from the wearable lower limb robot, achieved superior performance with an inference speed of 1.14 s. Specifically, the F-measure performance of the proposed model reached 96.17%, compared to 90.68% for DDLMI, 94.41% for DeepConvLSTM, and 95.57% for LSTM-CNN, respectively.

Effect of Annealing Temperature on the Structural and Electrochemical Properties of Hydrothermally Synthesized NiCo2O4 Electrodes.

In this study, a porous Ni-foam support was employed to enhance the capacitance of nickel cobaltite (NiCo2O4) electrodes designed for supercapacitors. The hydrothermal synthesis method was employed to grow NiCo2O4 as an active material on Ni-foam. The NiCo2O4 sample derived from hydrothermal synthesis underwent subsequent post-heat treatment at temperatures of 250 °C, 300 °C, and 350 °C. Thermogravimetric analysis of the NiCo2O4 showed that weight loss due to water evaporation occurs after 100 °C and enters the stabilization phase at temperatures above 400 °C. The XRD pattern indicated that NiCo2O4 grew into a spinel structure, and the TEM results demonstrated that the diffraction spots (DSs) on the (111) plane of the sample annealed at 350 °C were more pronounced than those of other samples. The specific capacitance of the NiCo2O4 electrodes exhibited a decrease with increasing current density across all samples, irrespective of the annealing temperature. The electrode annealed at 350 °C recorded the highest specific capacitance value. However, the capacity retention rate of the NiCo2O4 electrode revealed a deteriorating trend, declining to 88% at 250 °C, 75% at 300 °C, and 63% at 350 °C, as the annealing temperature increased.

The development of a web-based app employing machine learning for delirium prevention in long-term care facilities in South Korea.

BACKGROUND: Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability. METHODS: The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users). RESULTS: A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions. CONCLUSIONS: This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care.

Variations in the Physical Properties of RF-Sputtered CdS Thin Films Observed at Substrate Temperatures Ranging from 25 °C to 500 °C.

CdS films with a wide range of substrate temperatures as deposition parameters were fabricated on Corning Eagle 2000 glass substrates using RF magnetron sputtering. The crystallographic structure, microscopic surface texture, and stoichiometric and optical properties of each CdS film deposited at various substrate temperatures were observed to be highly temperature-dependent. The grown CdS thin films revealed a polycrystalline structure in which a cubic phase was mixed based on a hexagonal wurtzite phase. The relative intensity of the H(002)/C(111) peak, which represents the direction of the preferential growth plane, enhanced as the temperatures climbed from 25 °C to 350 °C. On the contrary, the intensity of the main growth peak at the higher temperatures of 450 °C and 500 °C was significantly reduced and exhibited amorphous-like behavior. The sharp absorption edge revealed in the transmission spectrum shifted from the long wavelength to the short wavelength region with the rise in the substrate temperature. The bandgap showed a tendency to widen from 2.38 eV to 2.97 eV when the temperatures increased from 25 °C to 350 °C. The CdS films grown at the temperatures of 450 °C and 500 °C exhibited glass-like transmittance with almost no interference fringes of light, which resulted in wide bandgap values of 3.09 eV and 4.19 eV, respectively.

Propensity-Score-Matched Evaluation of Adverse Events Affecting Recovery after COVID-19 Vaccination: On Adenovirus and mRNA Vaccines.

This study aimed to observe adverse events following immunisation (AEFIs) that affected recovery within two weeks after COVID-19 vaccination and investigate their risks in propensity-score-matched populations. Data were collected from 447,346 reports from the VAERS between 1 January 2021 and 31 July 2021. Propensity-score-matched populations were constructed by adjusting for demographic characteristics and 11 underlying diseases in eligible subjects who received 1 of 3 COVID-19 vaccines: 19,462 Ad26.COV2.S, 120,580 mRNA-1273, and 100,752 BNT162b2. We observed that 88 suspected AEFIs (22 in Ad26.COV2.S, 62 in mRNA-1273, and 54 in BNT162b2) were associated with an increased risk of delayed recovery within 2 weeks after COVID-19 vaccinations. Nervous system, musculoskeletal and connective tissue, gastrointestinal, skin, and subcutaneous tissue disorders were the most common AEFIs after COVID-19 vaccination. Interestingly, four local and systemic reactions affected recovery in different vaccine recipients during our study period: asthenic conditions and febrile disorders in Ad26.COV2.S and mRNA-1273; general signs and symptoms in mRNA-1273 and BNT162b2; injection site reactions in Ad26.COV2.S and BNT162b2. Although it is necessary to confirm a causal relationship with COVID-19 vaccinations, some symptoms, including paralysis, allergic disorders, breathing abnormalities, and visual impairment, may hinder the recovery of these recipients.

Machine Learning to Identify Psychomotor Behaviors of Delirium for Patients in Long-Term Care Facility.

This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients. A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 clinical variables and the survey items from the short confusion assessment method (S-CAM), and developed a mobile-based application. Multiple machine learning algorithms, including four rule-mining algorithms (C4.5, CBA, MCAR, and LEM2) and four other statistical learning algorithms (LR, ANNs, SVMs with three kernel functions, and random forest), were validated by paired Wilcoxon signed-rank tests on both macro-averaged F1 and weighted average F1-measures during the 10-times stratified 2-fold cross-validation. The LEM2 algorithm achieved the best prediction performance (macro-averaged F1-measure of 49.35%; weighted average F1-measure of 96.55%), correctly identifying adult patients at delirium risk. In the pairwise comparison between predictive powers observed from independent models, the LEM2 model showed a medium or large effect size between 0.4925 and 0.8766 when compared with LR, ANN, SVM with RBF, and MCAR models. We have confirmed that acute consciousness in S-CAM assessment is closely associated with different predictors for screening three psychomotor behaviors of delirium: 1) education level, dementia type or its level, sleep disorder, dehydration, and infection in mixed-type delirium; 2) gender, education level, dementia type, dehydration, bedsores, and foley catheter in hyperactive delirium; and 3) pain, sleep disorder, and haloperidol use in hypoactive delirium.

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance.

The deficiency and excess of vitamin D cause various diseases, necessitating continuous management; but it is not easy to accurately measure the serum vitamin D level in the body using a non-invasive method. The aim of this study is to investigate the correlation between vitamin D levels, body information obtained by an InBody scan, and blood parameters obtained during health checkups, to determine the optimum frequency of vitamin D quantification in the skin and to propose a vitamin D measurement method based on impedance. We assessed body composition, arm impedance, and blood vitamin D concentrations to determine the correlation between each element using multiple machine learning analyses and an algorithm which predicted the concentration of vitamin D in the body using the impedance value developed. Body fat percentage obtained from the InBody device and blood parameters albumin and lactate dehydrogenase correlated with vitamin D level. An impedance measurement frequency of 21.1 Hz was reflected in the blood vitamin D concentration at optimum levels, and a confidence level of about 75% for vitamin D in the body was confirmed. These data demonstrate that the concentration of vitamin D in the body can be predicted using impedance measurement values. This method can be used for predicting and monitoring vitamin D-related diseases and may be incorporated in wearable health measurement devices.

Structure, Luminescence, and Magnetic Properties of Crystalline Manganese Tungstate Doped with Rare Earth Ion.

The precursor prepared by co-precipitation method was sintered at various temperatures to synthesize crystalline manganese tungstate (MnWO4). Sintered MnWO4 showed the best crystallinity at a sintering temperature of 800 °C. Rare earth ion (Dysprosium; Dy3+) was added when preparing the precursor to enhance the magnetic and luminescent properties of crystalline MnWO4 based on these sintering temperature conditions. As the amount of rare earth ions was changed, the magnetic and luminescent characteristics were enhanced; however, after 0.1 mol.%, the luminescent characteristics decreased due to the concentration quenching phenomenon. In addition, a composite was prepared by mixing MnWO4 powder, with enhanced magnetism and luminescence properties due to the addition of dysprosium, with epoxy. To one of the two prepared composites a magnetic field was applied to induce alignment of the MnWO4 particles. Aligned particles showed stronger luminescence than the composite sample prepared with unsorted particles. As a result of this, it was suggested that it can be used as phosphor and a photosensitizer by utilizing the magnetic and luminescent properties of the synthesized MnWO4 powder with the addition of rare earth ions.

Enhanced Crystallinity and Luminescence Characteristics of Hexagonal Boron Nitride Doped with Cerium Ions According to Tempering Temperatures.

Hexagonal boron nitride was synthesized by pyrolysis using boric acid and melamine. At this time, to impart luminescence, rare earth cerium ions were added to synthesize hexagonal boron nitride nanophosphor particles exhibiting deep blue emission. To investigate the changes in crystallinity and luminescence according to the re-heating temperature, samples which had been subjected to pyrolysis at 900 °C were subjected to re-heating from 1100 °C to 1400 °C. Crystallinity and luminescence were enhanced according to changes in the reheating temperature. The synthesized cerium ion-doped hexagonal boron nitride nanoparticle phosphor was applied to the anti-counterfeiting field to prepare an ink that can only be identified under UV light.

Structure and Photoluminescence Properties of Rare-Earth (Dy3+, Tb3+, Sm3+)-Doped BaWO4 Phosphors Synthesized via Co-Precipitation for Anti-Counterfeiting.

Barium tungstate (BaWO4) powders with various sintering temperatures, and BaWO4:Dy3+ phosphor samples with concentrations of different rare-earth (RE) activator ions (Dy3+, Sm3+, Tb3+) were prepared through co-precipitation. The structural, morphological, and photoluminescent characteristics of barium tungstate phosphors depend on the concentration of RE ions. The crystallographic characteristics of the synthesized BaWO4 were analyzed using X-ray diffraction (XRD) patterns. The size and shape of the crystalline particles were estimated based on images measured with a field emission scanning electron microscope (FE-SEM). As the sintering temperature of the BaWO4 particles increased from 400 °C to 1000 °C, the size of the particles gradually increased and showed a tendency to clump together. In the sample doped with 7 mol % Dy3+ ions, the intensity of all emission bands reached their maximum. The emission spectra of the RE3+-doped BaWO4 powders by excitation at 325 nm were composed of yellow (Dy3+), red (Sm3+), and green (Tb3+) band at 572, 640, and 544 nm. This indicates that most of the RE3+ ions absorbed the position without reversal symmetry in the BaWO4 lattice. These results propose that strong emission intensity and tunable color for the phosphors can be accomplished by rare-earth doped host with an suitable quantity. In addition, the phosphor thin films, having high transparency from aqueous colloidal solutions, were deposited on banknotes, and it is considered whether it is suitable for anti-counterfeiting applications.

[Development of a Wellness Index for Workers].

PURPOSE: The purpose of this study was to develop a wellness index for workers (WIW) and examine the validity and reliability of the WIW for assessing workers' wellness. METHODS: The developmental process for the instrument included construction of a conceptual framework based on a wellness model, generation of initial items, verification of content validity, preliminary study, extraction of final items, and psychometric testing. Content validity was verified by 4 experts from occupational health nursing and wellness disciplines. The construct validity, convergent validity and discriminant validity were examined with confirmatory factor analysis. The reliability was examined with Cronbach's alpha. The participants were 494 workers from two workplaces. RESULTS: Eighteen items were selected for the final scale, and the results of the confirmatory factor analysis supported a five-factor model of wellness with acceptable model fit, and factors named as physical · emotional · social · intellectual · occupational wellness. The convergent and discriminant validity were also supported. The Cronbach's alpha coefficient was .91. CONCLUSION: The results indicate that the WIW is a valid and reliable instrument to comprehensively assess workers' wellness, and to provide basic directions for developing workplace wellness program.

Association rules to identify complications of cerebral infarction in patients with atrial fibrillation.

OBJECTIVES: The purpose of this study was to find risk factors that are associated with complications of cerebral infarction in patients with atrial fibrillation (AF) and to discover useful association rules among these factors. METHODS: The risk factors with respect to cerebral infarction were selected using logistic regression analysis with the Wald's forward selection approach. The rules to identify the complications of cerebral infarction were obtained by using the association rule mining (ARM) approach. RESULTS: We observed that 4 independent factors, namely, age, hypertension, initial electrocardiographic rhythm, and initial echocardiographic left atrial dimension (LAD), were strong predictors of cerebral infarction in patients with AF. After the application of ARM, we obtained 4 useful rules to identify complications of cerebral infarction: age (>63 years) and hypertension (Yes) and initial ECG rhythm (AF) and initial Echo LAD (>4.06 cm); age (>63 years) and hypertension (Yes) and initial Echo LAD (>4.06 cm); hypertension (Yes) and initial ECG rhythm (AF) and initial Echo LAD (>4.06 cm); age (>63 years) and hypertension (Yes) and initial ECG rhythm (AF). CONCLUSIONS: Among the induced rules, 3 factors (the initial ECG rhythm [i.e., AF], initial Echo LAD, and age) were strongly associated with each other.

Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches.

The accurate diagnosis of heart failure in emergency room patients is quite important, but can also be quite difficult due to our insufficient understanding of the characteristics of heart failure. The purpose of this study is to design a decision-making model that provides critical factors and knowledge associated with congestive heart failure (CHF) using an approach that makes use of rough sets (RSs) and decision trees. Among 72 laboratory findings, it was determined that two subsets (RBC, EOS, Protein, O2SAT, Pro BNP) in an RS-based model, and one subset (Gender, MCHC, Direct bilirubin, and Pro BNP) in a logistic regression (LR)-based model were indispensable factors for differentiating CHF patients from those with dyspnea, and the risk factor Pro BNP was particularly so. To demonstrate the usefulness of the proposed model, we compared the discriminatory power of decision-making models that utilize RS- and LR-based decision models by conducting 10-fold cross-validation. The experimental results showed that the RS-based decision-making model (accuracy: 97.5%, sensitivity: 97.2%, specificity: 97.7%, positive predictive value: 97.2%, negative predictive value: 97.7%, and area under ROC curve: 97.5%) consistently outperformed the LR-based decision-making model (accuracy: 88.7%, sensitivity: 90.1%, specificity: 87.5%, positive predictive value: 85.3%, negative predictive value: 91.7%, and area under ROC curve: 88.8%). In addition, a pairwise comparison of the ROC curves of the two models showed a statistically significant difference (p<0.01; 95% CI: 2.63-14.6).

A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis.

BACKGROUND: The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules. METHODS: We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing. RESULTS: Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data. CONCLUSIONS: The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis.

A new method for non-invasive measurement of skin in the low frequency range.

OBJECTIVES: The purpose of our study was to estimate skin structure and conductivity distribution in a cross section of local tissue using non-invasive measurement of impedance data. The present study was designed to evaluate the efficiency of skin depth information through computer simulations. The multilayer tissue model was composed of epidermis, dermis tissues, and subcutaneous. METHODS: In this study, electrical characteristics of skin models were used for conductivity of 0.13 S/m, 0.26 S/m, 0.52 S/m, permittivity of 94,000 F/m, and a frequency of 200 Hz. The effect of the new method was assessed by computer simulations using three-electrode methods. A non-invasive electrical impedance method has been developed for analysis using computer simulation and a skin electrical model with low frequency range. Using the three-electrode method differences through the potentials between measurement electrodes and reference electrodes can be easily detected. The Cole electrical impedance model, which is better suited for skin was used in this study. RESULTS: In this study, experiments using three-electrode methods were described by computer simulation based on a simple model. This electrical impedance model was fitted and developed in comparison with our model for measurement of skin impedance. CONCLUSIONS: The proposed electrical model for skin is suitable for use in interpretation of changes in impedance characterization of the skin. Using the computer simulation method, information on skin impedance depth can be more accurately developed and predicted.

Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis.

OBJECTIVES: Congenital muscular torticollis, a common disorder that refers to the shortening of the sternocleidomastoid in infants, is sensitive to correction through physical therapy when treated early. If physical therapy is unsuccessful, surgery is required. In this study, we developed a support vector regression model for congenital muscular torticollis to investigate the prognosis of the physical therapy treatent in infants. METHODS: Fifty-nine infants with congenital muscular torticollis received physical therapy until the degree of neck tilt was less than 5°. After treatment, the mass diameter was reevaluated. Based on the data, a support vector regression model was applied to predict the prognoses. RESULTS: 10-, 20-, and 50-fold cross-tabulation analyses for the proposed model were conducted based on support vector regression and conventional multi-regression method based on least squares. The proposed methodbased on support vector regression was robust and enabled the effective analysis of even a small amount of data containing outliers. CONCLUSIONS: The developed support vector regression model is an effective prognostic tool for infants with congenital muscular torticollis who receive physical therapy.

Implementation of Chest X-ray Observation Report Entry System.

OBJECTIVES: X-rays are widely used in medical examinations. In particular, chest X-rays are the most frequent imaging test. However, observations are usually recorded in a free-text format. Therefore, it is difficult to standardize the information provided to construct a database for the sharing of clinical data. Here, we describe a simple X-ray observation entry system that can interlock with an electronic medical record system. METHODS: We investigated common diagnosis indices. Based on the indices, we have designed an entry system which consists of 5 parts: 1) patient lists, 2) image selection, 3) diagnosis result entry, 4) image view, and 5) main menu. The X-ray observation results can be extracted in an Excel format. RESULTS: The usefulness of the proposed system was assessed in a study using over 500 patients' chest X-ray images. The data was readily extracted in a format that allowed convenient assessment. CONCLUSIONS: We proposed the chest X-ray observation entry system. The proposed X-ray observation system, which can be linked with an electronic medical record system, allows easy extraction of standardized clinical information to construct a database. However, the proposed entry system is limited to chest X-rays and it is impossible to interpret the semantic information. Therefore, further research into domains using other interpretation methods is required.