Fabrication of a Capacitive 3D Spacer Fabric Pressure Sensor with a Dielectric Constant Change for High Sensitivity.

Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique properties of conductive particles lead to nonlinear variations in the dielectric constant when pressure is applied, consequently affecting the gauge factor. The results reveal that while the fabric without CNT particles had a gauge factor of 1.967, the inclusion of 0.04 wt% CNT increased it significantly to 5.210. As sensor sensitivity requirements vary according to the application, identifying the necessary CNT wt% is crucial. Artificial intelligence, particularly the Multilayer Perception (MLP) model, enables nonlinear regression analysis for this purpose. The MLP model created and validated in this research showed a high correlation coefficient of 0.99564 between the model predictions and actual target values, indicating its effectiveness and reliability.

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations.

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

Measurement of 3D Wrist Angles by Combining Textile Stretch Sensors and AI Algorithm.

The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily produced by immersing an E-band in a SWCNT solution. The lightweight, cost-effective, and reproducible nature of textile stretch sensors makes them well suited for practical applications in clothing. In this paper, wrist angles were measured by attaching textile stretch sensors to an arm sleeve. Three sensors were utilized to measure all three axes of the wrist. Additionally, sensor precision was heightened through the utilization of the Multi-Layer Perceptron (MLP) technique, a subtype of deep learning. Rather than fixing the measurement values of each sensor to specific axes, we created an algorithm utilizing the coupling between sensors, allowing the measurement of wrist angles in three dimensions. Using this algorithm, the error angle of wrist angles measured with textile stretch sensors could be measured at less than 4.5°. This demonstrated higher accuracy compared to other soft sensors available for measuring wrist angles.

Comparative Performance Analysis of Inverse Phase Active Vibration Cancellation Using Macro Fiber Composite (MFC) and Vibration Absorption of Silicone Gel for Vibration Reduction.

This study focuses on addressing the issue of unwanted vibrations commonly encountered in various fields by designing an Active Vibration Cancellation (AVC) structure using a flexible piezoelectric composite material macro fiber composite (MFC). A comparative performance analysis was conducted between the AVC and a traditional passive gel that continuously absorbs vibrations. The results showed that AVC was more effective in mitigating vibrations, making it a promising solution for vibration control. The results of this study from extensive vibration-sensing experiments and comparisons revealed that AVC effectively cancels the vibrations and vibration absorption performance of the passive gel. These findings underline the potential of AVC as an efficient method for eliminating and managing undesired vibrations in practical applications. Specifically, AVC demonstrated a high vibration cancellation ratio of approximately 0.96 at frequencies above 10 Hz. In contrast, passive gel exhibited a relatively consistent vibration absorption ratio, approximately 0.70 to 0.75 at all tested frequencies. These quantitative findings emphasize the superior performance of AVC in reducing vibrations to levels below a certain threshold, demonstrating its efficacy for vibration control in real-world scenarios.

Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

The Programmable Design of Large-Area Piezoresistive Textile Sensors Using Manufacturing by Jacquard Processing.

Among wearable e-textiles, conductive textile yarns are of particular interest because they can be used as flexible and wearable sensors without affecting the usual properties and comfort of the textiles. Firstly, this study proposed three types of piezoresistive textile sensors, namely, single-layer, double-layer, and quadruple-layer, to be made by the Jacquard processing method. This method enables the programmable design of the sensor's structure and customizes the sensor's sensitivity to work more efficiently in personalized applications. Secondly, the sensor range and coefficient of determination showed that the sensor is reliable and suitable for many applications. The dimensions of the proposed sensors are 20 × 20 cm, and the thicknesses are under 0.52 mm. The entire area of the sensor is a pressure-sensitive spot. Thirdly, the effect of layer density on the performance of the sensors showed that the single-layer pressure sensor has a thinner thickness and faster response time than the multilayer pressure sensor. Moreover, the sensors have a quick response time (<50 ms) and small hysteresis. Finally, the hysteresis will increase according to the number of conductive layers. Many tests were carried out, which can provide an excellent knowledge database in the context of large-area piezoresistive textile sensors using manufacturing by Jacquard processing. The effects of the percolation of CNTs, thickness, and sheet resistance on the performance of sensors were investigated. The structural and surface morphology of coating samples and SWCNTs were evaluated by using a scanning electron microscope. The structure of the proposed sensor is expected to be an essential step toward realizing wearable signal sensing for next-generation personalized applications.

Effects of 3D Printing-Line Directions for Stretchable Sensor Performances.

Health monitoring sensors that are attached to clothing are a new trend of the times, especially stretchable sensors for human motion measurements or biological markers. However, price, durability, and performance always are major problems to be addressed and three-dimensional (3D) printing combined with conductive flexible materials (thermoplastic polyurethane) can be an optimal solution. Herein, we evaluate the effects of 3D printing-line directions (45°, 90°, 180°) on the sensor performances. Using fused filament fabrication (FDM) technology, the sensors are created with different print styles for specific purposes. We also discuss some main issues of the stretch sensors from Carbon Nanotube/Thermoplastic Polyurethane (CNT/TPU) and FDM. Our sensor achieves outstanding stability (10,000 cycles) and reliability, which are verified through repeated measurements. Its capability is demonstrated in a real application when detecting finger motion by a sensor-integrated into gloves. This paper is expected to bring contribution to the development of flexible conductive materials-based on 3D printing.

Patterns in renal diseases diagnosed by kidney biopsy: A single-center experience.

BACKGROUND: The worldwide incidence of renal disease diagnosed by a kidney biopsy varies with age, race, sex, and region. Owing to a lack of studies and limited research resources for this disease in Korea, we investigated renal disease patterns by analyzing data from kidney biopsies performed over 13 years in a university-based teaching hospital in Korea. METHODS: Among 2,053 kidney biopsies performed from 2001 to 2013 at Kyungpook National University Hospital, 1,924 were retrospectively analyzed for histopathologic, demographic, and clinical data as well as laboratory results. RESULTS: Among the 1,924 studied kidney biopsies, 1,078 were males (56.0%) and the mean age was 37.7 ± 16.5 years. Asymptomatic urinary abnormalities were the most common clinical manifestation (62.5%). Immunoglobulin A nephropathy (IgAN) was the most common primary glomerular disease (37.4%), followed by minimal change disease (MCD), membranous nephropathy (MN), focal segmental glomerulonephritis and crescentic glomerulonephritis. Secondary glomerular diseases accounted for 10.3% of the total biopsies, with lupus nephritis being the most common (4.6%) followed by Henoch-Schönlein purpura nephritis and diabetic nephropathy. The most common cause of nephrotic syndrome was MCD (42.1%) followed by MN. Among patients seropositive for hepatitis B or C, IgAN (28.3% and 21.4%, respectively) was the most common cause. CONCLUSION: IgAN and lupus nephritis were the most common primary and secondary glomerular diseases, respectively. Race, region, and practice patterns may affect renal disease patterns in different cohorts.

Vitamin D deficiency is associated with increased risk of bacterial infections after kidney transplantation.

BACKGROUND/AIMS: There may be an association between vitamin D levels and allograft outcomes in kidney transplant recipients (KTRs). However, few studies have been conducted to determine the association between vitamin D levels and post-transplant infections. This study investigated the impact of vitamin D deficiency on the risk of infection after kidney transplantation. METHODS: We measured 25-hydroxyvitamin D (25(OH)D) levels prior to kidney transplantation. Vitamin D deficiency was defined as a serum 25(OH)D level < 20 ng/mL. We examined the incidence of various post-transplant infections during follow-up period. We used Cox proportional hazards regression analysis to determine factors associated with increased risk of post-transplant infections during the follow-up period. RESULTS: A total of 164 KTRs were followed up for a mean of 24.8 ± 10.7 months. Among them, 135 patients (82.3%) had vitamin D deficiency. Patients with vitamin D deficiency had a significantly higher incidence of urinary tract infection (p = 0.027) and any bacterial infection (p = 0.010) compared to those without vitamin D deficiency. Vitamin D deficiency was not significantly associated with incidence of viral or fungal infections. Cox proportional hazards regression analysis revealed that vitamin D deficiency (hazard ratio, 11.07; 95% confidence interval, 1.46 to 84.03; p = 0.020) was independent risk factor for post-transplant bacterial infections. CONCLUSIONS: Pre-transplant vitamin D deficiency was a significant risk factor for bacterial infections after kidney transplantation. Further studies are needed on possible benefits of vitamin D supplementation for preventing post-transplant bacterial infection.

Low prealbumin levels are independently associated with higher mortality in patients on peritoneal dialysis.

BACKGROUND: Prealbumin, a sensitive marker for protein-energy status, is also known as an independent risk factor for mortality in hemodialysis patients. We investigated the impact of prealbumin on survival in incident peritoneal dialysis (PD) patients. METHODS: In total, 136 incident PD patients (mean age, 53.0 ± 15.8 years) between 2002 and 2007 were enrolled in the study. Laboratory data, dialysis adequacy, and nutritional parameters were assessed 3 months after PD initiation. Patients were classified into 2 groups according to prealbumin level: high prealbumin (≥ 40 mg/dL) and low prealbumin (< 40 mg/dL). RESULTS: The patients in the low-prealbumin group were older and had more comorbidities such as diabetes and cardiovascular diseases compared with the patients in the high-prealbumin group. Mean subjective global assessment scores were lower, and the high-sensitivity C-reactive protein levels were higher in the low-prealbumin group. Serum creatinine, albumin, and transferrin levels; percent lean body mass; and normalized protein catabolic rate were positively associated, whereas subjective global assessment scores and high-sensitivity C-reactive protein levels were negatively associated with prealbumin concentration. During the median follow-up of 49 months, patients in the lower prealbumin group had a higher mortality rate. Multivariate analysis revealed that prealbumin < 40 mg/dL (hazard ratio, 2.30; 95% confidence interval, 1.14-4.64) was an independent risk factor for mortality. In receiver operating characteristic curves, the area under the curve of prealbumin for mortality was the largest among the parameters. CONCLUSION: Prealbumin levels were an independent and sensitive predictor for mortality in incident PD patients, showing a good correlation with nutritional and inflammatory markers.

Duration of anuria predicts recovery of renal function after acute kidney injury requiring continuous renal replacement therapy.

BACKGROUND/AIMS: Little is known regarding the incidence rate of and factors associated with developing chronic kidney disease after continuous renal replacement therapy (CRRT) in acute kidney injury (AKI) patients. We investigated renal outcomes and the factors associated with incomplete renal recovery in AKI patients who received CRRT. METHODS: Between January 2011 and November 2013, 408 patients received CRRT in our intensive care unit. Of them, patients who had normal renal function before AKI and were discharged without maintenance renal replacement therapy (RRT) were included in this study. We examined the incidence of incomplete renal recovery with an estimated glomerular filtration rate < 60 mL/min/1.73 m(2) and factors that increased the risk of incomplete renal recovery after AKI. RESULTS: In total, 56 AKI patients were discharged without further RRT and were followed for a mean of 8 months. Incomplete recovery of renal function was observed in 20 of the patients (35.7%). Multivariate analysis revealed old age and long duration of anuria as independent risk factors for incomplete renal recovery (odds ratio [OR], 1.231; 95% confidence interval [CI], 1.041 to 1.457; p = 0.015 and OR, 1.064; 95% CI, 1.001 to 1.131; p = 0.047, respectively). In a receiver operating characteristic curve analysis, a cut-off anuria duration of 24 hours could predict incomplete renal recovery after AKI with a sensitivity of 85.0% and a specificity of 66.7%. CONCLUSIONS: The renal outcome of severe AKI requiring CRRT was poor even in patients without further RRT. Long-term monitoring of renal function is needed, especially in severe AKI patients who are old and have a long duration of anuria.