Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an F1-score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.

Effects of Receiver Beamforming for Vital Sign Measurements Using FMCW Radar at Various Distances and Angles.

Short-range millimeter wave radar sensors provide a reliable, continuous and non-contact solution for vital sign extraction. Off-The-Shelf (OTS) radars often have a directional antenna (beam) pattern. The transmitted wave has a conical main lobe, and power of the received target echoes deteriorate as we move away from the center point of the lobe. While measuring vital signs, the human subject is often located at the center of the antenna lobe. Since beamforming can increase signal quality at the side (azimuth) angles, this paper aims to provide an experimental comparison of vital sign extraction with and without beamforming. The experimental confirmation that beamforming can decrease the error in the vital sign extraction through radar has so far not been performed by researchers. A simple, yet effective receiver beamformer was designed and a concurrent measurement with and without beamforming was made for the comparative analysis. Measurements were made at three different distances and five different arrival angles, and the preliminary results suggest that as the observation angle increases, the effectiveness of beamforming increases. At an extreme angle of 40 degrees, the beamforming showed above 20% improvement in heart rate estimation. Heart rate measurement error was reduced significantly in comparison with the breathing rate.

3D-Printed Load Cell Using Nanocarbon Composite Strain Sensor.

The development of a 3D-Printed Load Cell (PLC) was studied using a nanocarbon composite strain sensor (NCSS) and a 3D printing process. The miniature load cell was fabricated using a low-cost LCD-based 3D printer with UV resin. The NCSS composed of 0.5 wt% MWCNT/epoxy was used to create the flexure of PLC. PLC performance was evaluated under a rated load range; its output was equal to the common value of 2 mV/V. The performance was also evaluated after a calibration in terms of non-linearity, repeatability, and hysteresis, with final results of 2.12%, 1.60%, and 4.42%, respectively. Creep and creep recovery were found to be 1.68 (%FS) and 4.16 (%FS). The relative inferiorities of PLC seem to originate from the inherent hyper-elastic characteristics of polymer sensors. The 3D PLC developed may be a promising solution for the OEM/design-in load cell market and may also result in the development of a novel 3D-printed sensor.

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application.

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

Validation of noncontact cardiorespiratory monitoring using impulse-radio ultra-wideband radar against nocturnal polysomnography.

PURPOSE: Polysomnography (PSG) is a standard diagnostic test for obstructive sleep apnea (OSA). However, PSG requires many skin-contacted sensors to monitor vital signs of patients, which may also hamper patients' sleep. Because impulse-radio ultra-wideband (IR-UWB) radar can detect the movements of heart and lungs without contact, it may be utilized for vital sign monitoring during sleep. Therefore, we aimed to verify the accuracy and reliability of the breathing rate (BR) and the heart rate (HR) measured by IR-UWB radar. METHOD: Data acquisition with PSG and IR-UWB radar was performed simultaneously in 6 healthy volunteers and in 15 patients with suspected OSA. Subjects were divided into 4 groups (normal, mild OSA, moderate OSA, and severe OSA) according to the apnea-hypopnea index (AHI). BRs and HRs obtained from the radar using a software algorithm were compared with the BRs (chest belt) and the HRs (electrocardiography) obtained from the PSG. RESULTS: In normal and in mild OSA, BRs (intraclass correlation coefficients R [ICCR] 0.959 [0.956-0.961] and 0.957 [0.955-0.960], respectively) and HRs ([ICCR] 0.927 [0.922-0.931] and 0.926 [0.922-0.931], respectively) measured in the radar showed excellent agreement with those measured in PSG. In moderate and severe OSA, BRs ([ICCR] 0.957 [0.956-0.959] and 0.873 [0.864-0.882], respectively) and HRs ([ICCR] 0.907 [0.904-0.910] and 0.799 [0.784-0.812], respectively) from the two methods also agreed well. CONCLUSIONS: The IR-UWB radar could accurately measure BRs and HRs in sleeping patients with OSA. Therefore, IR-UWB radar may be utilized as a cardiopulmonary monitor during sleep.

Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier.

The emerging integration of technology in daily lives has increased the need for more convenient methods for human-computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man-machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.

Experimental Comparison of IR-UWB Radar and FMCW Radar for Vital Signs.

In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel.

An Overview of Signal Processing Techniques for Remote Health Monitoring Using Impulse Radio UWB Transceiver.

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

A New Thresholding Method for IR-UWB Radar-Based Detection Applications.

In this paper, we proposed a new thresholding method for impulse radio ultra-wideband (IR-UWB) radar-based detection applications by taking both the false alarm and miss-detection rates into consideration. The thresholding algorithm is the key point of the detection application, and there have been numerous studies on these developments. Most of these studies were related to the occurrence of false alarms, such as the constant false alarm rate algorithm (CFAR). However, very few studies have considered miss-detection, which is another crucial issue in detection applications. To mitigate this issue, our proposed algorithm considered miss-detection as well as the false alarms occurring during thresholding. In the proposed algorithm, a threshold is determined by combining a noise signal-based threshold, in which the focus point is the false alarm, with a target signal-based threshold, in which the focus point is a miss-detection, at a designed ratio. Therefore, a threshold can be determined based on the focus point by adjusting the designed ratio. In addition, the proposed algorithm can estimate the false alarm and miss-detection rates for the determined threshold, and thus, the threshold can be objectively set. Moreover, the proposed algorithm is better in terms of understanding the target signal for a given environment. A target signal can be affected by the clutter, installation height, and the angle of the radar, which are factors that noise-oriented algorithms do not consider. As the proposed algorithm analyzed the target signal, these factors were all considered. We analyzed the false alarm and miss-detection rates for the thresholds, which were determined by different combination ratios at various distances, and we experimentally verified the validity of the proposed algorithm.

Finger-Counting-Based Gesture Recognition within Cars Using Impulse Radar with Convolutional Neural Network.

The diversion of a driver's attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative interface inside cars. Given that cars are the targeted application area, we determined the optimal location for the radar sensor, so that the signal reflected from the driver's hand during gesturing is unaffected by interference from the motion of the driver's body or other motions within the car. We implemented a Convolutional Neural Network-based technique to recognize the finger-counting-based hand gestures using an Impulse Radio (IR) radar sensor. The accuracy of the proposed method was sufficiently high for real-world applications.

A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars.

Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

Quantified Activity Measurement for Medical Use in Movement Disorders through IR-UWB Radar Sensor.

Movement disorders, such as Parkinson's disease, dystonia, tic disorder, and attention-deficit/hyperactivity disorder (ADHD) are clinical syndromes with either an excess of movement or a paucity of voluntary and involuntary movements. As the assessment of most movement disorders depends on subjective rating scales and clinical observations, the objective quantification of activity remains a challenging area. The purpose of our study was to verify whether an impulse radio ultra-wideband (IR-UWB) radar sensor technique is useful for an objective measurement of activity. Thus, we proposed an activity measurement algorithm and quantitative activity indicators for clinical assistance, based on IR-UWB radar sensors. The received signals of the sensor are sufficiently sensitive to measure heart rate, and multiple sensors can be used together to track the positions of people. To measure activity using these two features, we divided movement into two categories. For verification, we divided these into several scenarios, depending on the amount of activity, and compared with an actigraphy sensor to confirm the clinical feasibility of the proposed indicators. The experimental environment is similar to the environment of the comprehensive attention test (CAT), but with the inclusion of the IR-UWB radar. The experiment was carried out, according to a predefined scenario. Experiments demonstrate that the proposed indicators can measure movement quantitatively, and can be used as a quantified index to clinically record and compare patient activity. Therefore, this study suggests the possibility of clinical application of radar sensors for standardized diagnosis.

A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar.

The vital sign monitoring through Impulse Radio Ultra-Wide Band (IR-UWB) radar provides continuous assessment of a patient's respiration and heart rates in a non-invasive manner. In this paper, IR UWB radar is used for monitoring respiration and the human heart rate. The breathing and heart rate frequencies are extracted from the signal reflected from the human body. A Kalman filter is applied to reduce the measurement noise from the vital signal. An algorithm is presented to separate the heart rate signal from the breathing harmonics. An auto-correlation based technique is applied for detecting random body movements (RBM) during the measurement process. Experiments were performed in different scenarios in order to show the validity of the algorithm. The vital signs were estimated for the signal reflected from the chest, as well as from the back side of the body in different experiments. The results from both scenarios are compared for respiration and heartbeat estimation accuracy.

Vital Sign Monitoring and Mobile Phone Usage Detection Using IR-UWB Radar for Intended Use in Car Crash Prevention.

In order to avoid car crashes, active safety systems are becoming more and more important. Many crashes are caused due to driver drowsiness or mobile phone usage. Detecting the drowsiness of the driver is very important for the safety of a car. Monitoring of vital signs such as respiration rate and heart rate is important to determine the occurrence of driver drowsiness. In this paper, robust vital signs monitoring through impulse radio ultra-wideband (IR-UWB) radar is discussed. We propose a new algorithm that can estimate the vital signs even if there is motion caused by the driving activities. We analyzed the whole fast time vital detection region and found the signals at those fast time locations that have useful information related to the vital signals. We segmented those signals into sub-signals and then constructed the desired vital signal using the correlation method. In this way, the vital signs of the driver can be monitored noninvasively, which can be used by researchers to detect the drowsiness of the driver which is related to the vital signs i.e., respiration and heart rate. In addition, texting on a mobile phone during driving may cause visual, manual or cognitive distraction of the driver. In order to reduce accidents caused by a distracted driver, we proposed an algorithm that can detect perfectly a driver's mobile phone usage even if there are various motions of the driver in the car or changes in background objects. These novel techniques, which monitor vital signs associated with drowsiness and detect phone usage before a driver makes a mistake, may be very helpful in developing techniques for preventing a car crash.

Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar.

Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces.

A Novel Successive Suturing Device for Laparoscopic Surgery.

Suturing is one of the more tiresome and difficult tasks during laparoscopic surgeries. To cope with this problem, we aimed to develop a novel successive suturing device. A novel needle holding and locking mechanism is proposed to transfer the needle between the upper and bottom jaws. The device is straightforward to use with intuitive 2-trigger control, and it can perform successive suturing without the need of reload between stiches. Also, it is compact enough to be inserted through a 12-mm trocar. The feasibility of the device is verified through in vitro and in vivo experiments. It was found that the developed device was able to successfully close the wounds without any leakage. The developed successive suturing device offers an easy way of performing suture, and it will greatly help surgeons during laparoscopic surgeries.

Microbial community, and biochemical and physiological properties of Korean traditional black raspberry (Robus coreanus Miquel) vinegar.

BACKGROUND: The aim of this study was to elucidate the changes in microbial community and biochemical and physiological properties of traditional Muju black raspberry (Robus coreanus Miquel) vinegar (TMBV) during fermentation by culture-independent methods. RESULTS: During vinegar fermentation, ethanol produced up to 120 g L(-1) until day 35, with continuously increasing yeast concentration to a total of log 7.6 CFU mL(-1) . After day 35, acetic acid bacteria (AAB) concentrations rose to log 5.8 CFU mL(-1) until day 144. Denaturing gradient gel electrophoresis analysis showed that Saccharomyces cerevisiae was detected until day 87 of the fermentation, at which point Acetobacter pasteurianus gradually took over as the dominant species. Total sugar was reduced to 6.6 °Brix and total acidity produced up to 44 g L(-1) . CONCLUSION: In this study, we established the physicochemical analysis and growth dynamics of yeast and AAB during alcoholic and acetic acid fermentation of black raspberry by a traditional method. Overall, S. cerevisiae and A. pasteurianus species appeared to dominate the TMBV fermentation. In conclusion, this study demonstrated a suitable fermentation system for TMBV by the static surface method. © 2015 Society of Chemical Industry.

Development of biopsy gun for aspiration and drug injection.

Tumor samples are required for pathological examinations, and different instruments are generally used to obtain samples of different types of tumors. Among the many methods available for obtaining tumor tissues, gun biopsy is widely used because it is much simpler than incisional biopsy and can collect many more samples than aspiration biopsy. However, conventional biopsy guns cannot simultaneously perform biopsy aspiration, bleeding prevention, and marker injection. In this study, we developed a biopsy gun that can simultaneously perform biopsy aspiration and sample collection, in addition to injecting a styptic agent and marker to prevent bleeding and contamination in the biopsy track. We then used a prototype to evaluate the feasibility of the developed device. The collectable sample size was also assessed. Performance of the biopsy aspiration feature was also evaluated, including the maximum aspiration viscosity. Finally, we tested the maximum amount of drug that can be injected. We found that the biopsy gun developed here is an alternative tool for biopsy collection with improved procedure safety and diagnostic accuracy.

A public dataset of dogs vital signs recorded with ultra wideband radar and reference sensors.

Recently, radar sensors have been extensively used for vital sign monitoring in dogs, owing to their noncontact and noninvasive nature. However, a public dataset on dog vital signs has yet to be proposed since capturing data from dogs requires special training and approval. This work presents the first ever ultra wideband radar-based dog vital sign (UWB-DVS) dataset, which was captured in two independent scenarios. In the first scenario, clinical reference sensors are attached to the fainted dogs, and data from UWB radar and reference sensors are captured synchronously. In the second scenario, the dogs can move freely, and video recordings are provided as a reference for movement detection and breathing extraction. For technical validation, a high correlation, above 0.9, is found between the radar and clinical reference sensors for both the heart rate and breathing rate measurements in scenario 1. In scenario 2, the vital signs and movement of the dogs are shown in the form of dashboards, demonstrating the long-term monitoring capability of the radar sensor.

Float-stacked graphene-PMMA laminate.

Semi-infinite single-atom-thick graphene is an ideal reinforcing material that can simultaneously improve the mechanical, electrical, and thermal properties of matrix. Here, we present a float-stacking strategy to accurately align the monolayer graphene reinforcement in polymer matrix. We float graphene-poly(methylmethacrylate) (PMMA) membrane (GPM) at the water-air interface, and wind-up layer-by-layer by roller. During the stacking process, the inherent water meniscus continuously induces web tension of the GPM, suppressing wrinkle and folding generation. Moreover, rolling-up and hot-rolling mill process above the glass transition temperature of PMMA induces conformal contact between each layer. This allows for pre-tension of the composite, maximizing its reinforcing efficiency. The number and spacing of the embedded graphene fillers are precisely controlled. Notably, we accurately align 100 layers of monolayer graphene in a PMMA matrix with the same intervals to achieve a specific strength of about 118.5 MPa g-1 cm3, which is higher than that of lightweight Al alloy, and a thermal conductivity of about 4.00 W m-1 K-1, which is increased by about 2,000 %, compared to the PMMA film.