Biosignal Measurement Research Articles

Cardiac autonomic changes after stroke

Abstract Introduction There is a highly complex and clinically important relationship between the cardiac and the nervous system, called the heart-brain axis [1]. Therefore, acute brain damage may lead to alterations in cardiac function due to changes in the autonomic nervous system[2,3]. Novel computerised biosignal analysis techniques and advanced biosignal assessment could provide a valuable tool to sensitively analyse cardiac autonomic function (CAF). CAF can be characterized by two complementary digital biomarkers, periodic repolariation dynamics (PRD) and deceleration capacity (DC). The aim of this study was to determine CAF by means of PRD and DC in patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA) and to analyze them according to their relationship with specific cardiac (high-sensitive Troponin T (hsTnT) and N-terminal pro-B-type natriuretic peptide (NT-pro BNP)) and stroke (location, type, severity) characteristics. Methoden / Methods Between February 22, 2021, and May 5, 2023, 282 patients after an AIS or TIA were enrolled in the study. During the inpatient stay, all relevant parameters were recorded and stored in a database. To assess CAF, a 30-minute biosignal measurement was performed with a high resolution (1kHz) electrocardiogram (ECG). Cardiac autonomic biomarkers, PRD and DC, were calculated semiautomatically afterwards from patients. Results Fifteen ECGs were excluded due to poor recording quality. Consequently, data from 261 AIS and 21 TIA patients were analyzed. A total of 105 patients (37.2 %) had pathological PRD (≥ 5.75 deg2) and/or DC (≤ 2.5 ms), 78 patients (27.7 %) had pathological PRD, and 57 patients (20.2 %) had pathological DC. The median PRD was 3.13 deg2 and the median DC was 5.30 ms (see Figure). There were no significant differences in the distribution of PRD and DC between male and female patients (PRD p = 0.38, DC p = 0.30), and between AIS and TIA patients (PRD p = 0.13, DC p = 0.66). Patients with middle cerebral artery (MCA) infarction had significantly higher median PRD values (3.56 deg2 [IQR 1.51 - 6.59 ] vs. 2.81 deg2 [IQR 1.15 - 5.99], p = 0.047) and lower median DC values (4.66 ms [IQR 2.65 – 7.76] vs. 6.11 ms [IQR 3.15 – 10.61], p = 0.011) than patients with stroke located in another cerebral regions (see Figure). Patients with pathological PRD or DC had significantly higher hsTnT (13.50 ng/l [IQR 9.7 – 24.1] vs. 11.05 ng/l [ IQR 7.13 – 16.23], p < 0.001) and NT-pro BNP (269.00 ng/l [IQR 117.50 – 687.00] vs. 125.50 ng/l [IQR 57.00 – 288.25], p < 0.001) values. Conclusions The study showed that a considerable proportion, more than one third of all patients, had evidence of cardiac autonomic dysfunction after acute cerebral ischaemia. Of particular interest, PRD was higher and DC was lower in patients with MCA ischaemia, reflecting a potentially higher cardiac risk profile. Cardiac autonomic dysfunction was also associated with increased hsTnT and NT-proBNP levels.

Hardware-software implementation of a local Wi-Fi network for the transmission of biomedical signals

The object of this study is a wireless local Wi-Fi network for broadcasting biomedical signals, its structure, and principles of construction. The task of minimizing the power consumption of a Wi-Fi transmitter has been addressed, which provides the possibility of building a wireless system for long-term monitoring of biomedical signals. As a result, a functional diagram of a wireless Holter monitoring system based on an ESP32 microcontroller was constructed, which includes a subsystem for setting up and diagnosing system units using MATLAB software packages, an ECG signal generator, and a multifunctional PCIe board from National Instruments. Evaluation criteria and methods for minimizing power consumption by an autonomous Wi-Fi transmitter have been proposed. Methods for synchronizing the working cycles of the transmitter and receiver of the Holter monitoring system were determined. A procedure for determining the optimal biosignal measurement frequency is presented, at which the distortion of ECG signals would be minimal, which means that the signal could be transmitted without losses. The concept of constructing an algorithm for implementing a program for a Wi-Fi transmitter has been developed, ensuring parallel execution of ECG signal measurement operations and their transmission over a local network. The data from semi-naturalistic tests with an experimental Holter monitoring system with a pre-setup subsystem and using external measuring devices, a computer, and the MATLAB software environment are presented. A comparative analysis of the experimental data with primary ECG signals and ECG signals at the receiver output showed a fairly stable correspondence between the input and output ECG signals. The proposed algorithms make it possible to reduce the average current consumption of the ESP32 microcontroller to 50.5 mA. The results of the study demonstrate the possibility of constructing an energy-efficient wireless system for long-term monitoring of biomedical signals based on the Wi-Fi interface

Progresses and Perspectives of 1D Soft Sensing Devices for Healthcare Applications

AbstractHealthcare sensing devices enable continuous monitoring of diverse biosignals, such as respiration, heartbeat, temperature, inflammation, Electroencephalogram (EEG), and biomechanical movement, contributing to the management of health conditions and early diagnosis of diseases. However, clinically available tools have several limitations for real‐time measurement of biosignals in non‐hospital environments owing to their cumbersome, complex designs, and rigidity. To address these limitations, there has been a growing body of research that explores flexible and soft electronics for healthcare applications, which feature high mechanical compliance and adaptability. Especially, sensing devices based on fiber structures have attracted significant attention due to high flexibility, lightweight design, effective workspace and unique structural adaptability. Moreover, 1D sensing devices can be seamlessly integrated into garments and the human body with complex structures without any unconformity. In this perspective, The fabrication and electrical functionalization of fiber substrates and focus on recent advances in various fiber‐based sensing systems for strain, pressure, temperature, pH, biomarkers, and neural activity is explored. Additionally, biodegradable fiber‐based sensing devices as future healthcare technologies are briefly discussed. Finally, this article provides a summary and outlook on the remaining challenges for current fiber‐based sensing devices.

Soft and Conductive Polyethylene Glycol Hydrogel Electrodes for Electrocardiogram Monitoring.

The measurement of biosignals in the clinical and healthcare fields is fundamental; however, conventional electrodes pose challenges such as incomplete skin contact and skin-related issues, hindering accurate biosignal measurement. To address these challenges, conductive hydrogels, which are valuable owing to their biocompatibility and flexibility, have been widely developed and explored for electrode applications. In this study, we fabricated a conductive hydrogel by mixing polyethylene glycol diacrylate (PEGDA) with poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) polymers dissolved in deionized water, followed by light-triggered crosslinking. Notably, this study pioneered the use of a PEGDA-PEDOT:PSS hydrogel for electrocardiogram (ECG) monitoring- a type of biosignal. The resulting PEGDA-PEDOT:PSS hydrogel demonstrated remarkable conductivity while closely approximating the modulus of skin elasticity. Additionally, it demonstrated biocompatibility and a high signal-to-noise ratio in the waveforms. This study confirmed the exceptional suitability of the PEGDA-PEDOT:PSS hydrogel for accurate biosignal measurements with potential applications in various wearable devices designed for biosignal monitoring.

CNN and Rf based Early Detection of Brain Stroke Using Bio-Electrical Signals

The brain is a vital component of the body that is in control of involuntary and voluntary movements such as walking, memory, and vision. Nowadays, some of the most prevalent brain disorders include Alzheimer's disease, brain tumors, and epilepsy (paralysis or stroke). As a result, stroke has become a significant global health concern, with high rates of mortality and disability. Importantly, approximately two-thirds of all strokes occur in developing countries, highlighting the significant burden of this condition in these regions. Therefore, emphasizing the timely detection and appropriate treatment of brain tumors is crucial. Given the high potential for mortality or severe disability associated with stroke disease, prioritizing active primary prevention and early identification of prognostic symptoms is of paramount importance. Ischemic stroke and hemorrhagic stroke are the two primary classifications for stroke diseases. Each type calls for specific emergency treatments, such as the administration of thrombolytics or coagulants, tailored to their respective underlying mechanisms. However, to effectively manage stroke, it is crucial to promptly identify the precursor symptoms in real-time, as they can vary among individuals. Timely professional treatment within the appropriate treatment window is essential and should be provided by a medical institution. In contrast, prior research has primarily centered around the formulation of acute treatment strategies or clinical guidelines subsequent to the occurrence of a stroke, rather than giving sufficient attention to the early identification of prognostic symptoms. Specifically, recent research has extensively utilized image analysis techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), as a primary approach for detecting and predicting prognostic symptoms in stroke patients.
 Traditional methodologies not only encounter difficulties in achieving early real-time diagnosis but also exhibit limitations in terms of prolonged testing duration and high testing costs. In this study, we introduce a novel system that employs machine learning techniques to predict and semantically interpret prognostic symptoms of stroke. Our approach utilizes real-time measurement of multi-modal bio-signals, namely electrocardiogram (ECG) and photoplethysmography (PPG), with a specific focus on the elderly population.
 To facilitate real-time prediction of stroke disease during walking, we have developed a stroke disease prediction system that incorporates a hybrid ensemble architecture. This architecture synergistically combines Convolutional Neural Network (CNN) and Random Forest (RF) models, enabling accurate and timely prognostication of stroke disease. The suggested method prioritises the convenience of use of bio-signal sensors for the elderly by collecting bio-signals from three electrodes placed on the index finger. These signals include ECG and PPG, and they are obtained while the participants walk. The CNN-RF model delivers satisfactory prediction accuracy when using raw ECG and PPG data. F1-Score, Sensitivity, Specificity, and Accuracy were the performance parameters used to evaluated the model's performance.

Investigation of performance and energy cost of automobile air-conditioner using alternative refrigerants based on thermal comfort using various cooling seats

During hot summer days, the rapid rise in temperature inside a vehicle and the direct transmission of radiant energy cause significant discomfort to drivers, and the driving distance of an electric vehicle is greatly reduced due to the operation of the air-conditioner. Therefore, it is essential to provide a thermally comfortable environment to the driver and reduce the energy consumption of air-conditioners in the EV. This study investigated the thermal comfort of the driver according to the use of a basic seat (BSS), ventilation seat (VLS), and cold-water seat (CWS) in the car cabin under summer condition. Also, based on thermal comfort, the system performance and energy cost of an automobile air-conditioner using various alternative refrigerants were analyzed and compared. As a result, when the CWS was used, the stress index of subjects was the lowest in the PPG, and the relative β/α of the subjects decreased in the EEG. In addition, the results of the subjective questionnaires and bio-signal measurement showed the most thermally comfortable when using the CWS. The COPs of the air-conditioner using R134a, R1234yf, R1234ze(E), and R152a increased by 19.3 %, 18.9 %, 19.0 %, and 19.3 %, respectively, and annual energy consumption of the air-conditioner decreased. In addition, in the five European countries, the annual electric charging costs and annual eq-CO2 emissions for using the CWS showed the most significant reduction in Germany. Therefore, it was confirmed that using CWS can provide a thermally comfortable environment for drivers inside the vehicle during summer and effectively reduce power consumption used for air conditioning in EVs.

Modular vertically-integrated skin patch for biosignal measurements

This paper presents research on a novel and modular vertically-integrated wearable skin patch for biosignal measurements. The flexible patch consists of a disposable skin-contacting electrode layer and a durable electronics part. The durable part is designed to include a printed re-chargeable battery, a solar cell, electronics for the measurement of electrocardiogram (ECG), galvanic skin response, acceleration, temperature and humidity and a covering visually appealing jewellery-like functional crystal layer for decoration and user interaction. The patch can store measurement data and transmit it to a mobile phone via a bluetooth low-energy radio. Integration process is developed for the vertical stacking that limits the skin-patch area to achieve a desirable form factor. The sensing electrodes are printed on stretchable thermoformable polyurethane substrate with vias through the substrate to couple skin-contacting electrodes to the upper functional layers. A removable adhesive layer between the disposable and durable parts is developed to enable separating the two parts after wear and then to couple the durable part with a new disposable part. The patch is tested on body for ECG sensing.

Permeable Skin Patch with Miniaturized Octopus-Like Suckers for Biosignal Monitoring.

Wearable electronics demand high adhesion properties through various skin conditions. Here, 3D-printed porous skin patches with octopus-like suckers of different geometries are presented. Experimental and theoretical studies are investigated to show an enhanced, low-cost 3D-printed bioinspired patches that successfully obtain biosignals comparable to commercial electrodes.Clinical Relevance- This work establishes low-cost, highly-adhesive skin patches that are irritation- and contamination-free with effortless peel-off technique for biosignal measurement.

Remote Bio Vision: Perfusion Imaging Based Non-Contact Autonomic Biosignal Measurement Algorithm

Due to the outbreak of new viruses in recent years, the need for contactless medical systems has significantly increased for monitoring, treating, managing, and preventing diseases. Systems are constantly being developed to replace conventional biosignal measuring methods with contactless medical systems. Several systems that substitute attachable sensors in conventional systems have been developed to measure diverse biosignals using images captured with visible cameras. Most non-contact biometric systems have certain limitations, for example, they can only measure a single biosignal, such as the heart rate, oxygen saturation, or blood pressure. They are also easily affected by external factors such as lighting conditions and noises. Furthermore, a blood pressure measuring system based on the pulse transit time requires physical contact and accuracy depends on the measurement location. This study examined a system that provides highly accurate biosignals to overcome these limitations of conventional biosignal measurement systems. This study proposes a biosignal measurement system that uses an infra-red camera to reduce visible light noise. The proposed system uses light sources outside the visible range and measures the amplified reflected light using a near infra-red camera to calculate the heart rate and oxygen saturation. The blood flow value is calculated by the measured heart rate and oxygen saturation. Based on measured data, blood pressure is estimated without contact with the body. We compared the proposed system with an existing non-contact blood pressure measurement system. The proposed system showed an average error rate of 2.07% in blood pressure measurement, which increased by 3.86% compared to the conventional system. Also, this study enabled the early detection of pressure ulcers, which is difficult to measure quantitatively.

Efficient and Low Color Information Dependency Skin Segmentation Model

Skin segmentation involves segmenting the human skin region in an image. It is a preprocessing technique mainly used in many applications such as face detection, hand gesture recognition, and remote biosignal measurements. As the performance of skin segmentation directly affects the performance of these applications, precise skin segmentation methods have been studied. However, previous skin segmentation methods are unsuitable for real-world environments because they rely heavily on color information. In addition, deep-learning-based skin segmentation methods incur high computational costs, even though skin segmentation is mainly used for preprocessing. This study proposes a lightweight skin segmentation model with a high performance. Additionally, we used data augmentation techniques that modify the hue, saturation, and values, allowing the model to learn texture or contextual information better without relying on color information. Our proposed model requires 1.09M parameters and 5.04 giga multiply-accumulate. Through experiments, we demonstrated that our proposed model shows high performance with an F-score of 0.9492 and consistent performance even for modified images. Furthermore, our proposed model showed a fast processing speed of approximately 68 fps, based on 3 × 512 × 512 images and an NVIDIA RTX 2080TI GPU (11GB VRAM) graphics card.

신기술 분야 학문분류체계 제안 연구

In this study, to propose a classification system for new technology academic fields, the current state of domestic and foreign new technology academic classification system was analyzed and implications were derived. Based on this, the establishment (draft) of the academic classification system for 21 new technology fields was proposed. As a result, the artificial intelligence application in the field of artificial intelligence and the artificial intelligence platform part were integrated, and the items were adjusted by deleting the subcategories for similar items. Second, in the case of big data, a big data classification system was proposed by reflecting the opinion that service and academic perspectives are mixed. Third, in the case of next-generation semiconductors, a new technology classification system was proposed based on the common recognition of those working in the industry, academia and research institutes. Fourth, in the case of customized health care, health care software, digital treatment study, dormant health (bio living body), medical device development study using bio-signal measurement and information, bio-health regulation and licensing, etc. were included. Lastly, the classification of the new energy industry was readjusted by referring to the National Science and Technology Standard Classification System.

Breathable, Flexible, Transparent, Hydrophobic, and Biotic Sustainable Electrodes for Heating and Biopotential Signal Measurement Applications

Pressure to reduce the global amount of e‐waste has increased in recent years. The optimal use of natural resources is a demanding area especially due to the overabundance of the use of resources and challenges with after‐life disposal. Herein, an easy method is developed to fabricate an improved version of leaf skeleton‐based biodegradable, transparent, flexible, and hydrophobic electrodes. A fractal‐like rubber leaf skeleton is used as the substrate, physical vapor deposited Au interlayer to promote adhesion, and uniform deposition of overlayer silver nanowires. The fabricated surfaces present a high level of electrical stability, optical transparency, hydrophobicity, and robust mechanical properties. The prepared electrodes demonstrate a comparable level of optical transmittance to the virgin leaf skeleton. The mechanical sturdiness of the electrodes is verified by 1k bending cycles. To demonstrate the functionality of these hybrid biotic conductive network (HBCN) electrodes, their performance is evaluated as flexible transparent heating elements and as biosignal measurement electrodes. The heater can reach a temperature of 140 °C with only 2.5 V in ≈5 s and Ag nanowire loading of ≈160 μg cm−2. Likewise, electrocardiogram (ECG) and electromyogram (EMG) signals are successfully obtained from the electrodes without using any electrode gel or other electrolytes.

Failure mode and effects analysis of telehealth service of minority elderly for sustainable digital transformation

BackgroundTelehealth services are time- and cost-saving solutions for disease management for older adults. Minority older individuals with multiple risk factors have an increasing demand for telehealth services. There are insufficient data on patient safety in telehealth services. This study aimed to enhance the quality of telehealth services by reducing errors and creating a safe user environment for low-income older adults. Failure mode and effects analysis tool (FMEA) was adopted to manage potential risks for sustainable digital transformation. MethodAn eight-member multidisciplinary team conducted telehealth FMEA to determine risk priority numbers (RPNs). The process included identifying the potential cause and effect failure mode of each step; measuring severity, probability, and detectability scores for RPNs; and generating strategies to decrease potential failures. ResultsThis study identified 24 risk factors and 34 causes in four major phases with a mean RPN of 90.7: preparation to measure biosignals, measurement of biosignals following instructions from a personal device, confirmation of measurement results, and intervention based on disease or condition type. Risk prioritization revealed four high failure modes and a total RPN of 362.7. Based on fundamental causes, risks were categorized as oblivescence, economic issues, and technology literacy. ConclusionsTo correct these failure modes, stabilization of the platform, adding to the providers’ manpower, and support for government policies are recommended. FMEA identifies and evaluates the potential risks of telehealth services. The selected priorities reduce the clinical risks of low-income elders who use telehealth services by weighting clinical actions.

Evaluation of a Wearable System for Fetal ECG Monitoring Using Cooperative Sensors.

Fetal electrocardiography (fECG) has gotten widespread interest in the last years as technology for fetal monitoring. Compared to cardiotocography (CTG), the current state of the art, it can be designed in smaller formfactor and is thus suited for long-term and unsupervised monitoring. In the present study we evaluated a wearable system which is based on CSEM's cooperative sensors, a versatile technology that allows for the measurement of multiple biosignals and an easy integration into a garment or patch. The system was tested on 25 patients with singleton pregnancies and an age of gestation ≥ 37 weeks. To reject unreliable fetal heart rate (fHR) estimations, the signal processing algorithm provides a signal quality index. In 12 out of 21 patients available for analysis, a good performance of fHR estimations was obtained with a mean absolute error < 5 bpm and an acceptance rate >70%. However, the remaining 9 patients showed low acceptance rates and high errors. Besides investigating the source of these high errors, future work includes the investigating improved signal processing algorithms, different body positions and the use of dry electrodes. Clinical Relevance - The aim of this work is to develop a wearable system that can be offered in hospitals as an alternative to cardiotocography, or as a home monitoring tool for at risk fetuses, in the era of evolving telemedicine.

Blood Pressure Response and Pulse Arrival Time During Exercise Testing in Well-Trained Individuals.

Introduction: There is a lack of data describing the blood pressure response (BPR) in well-trained individuals. In addition, continuous bio-signal measurements are increasingly investigated to overcome the limitations of intermittent cuff-based BP measurements during exercise testing. Thus, the present study aimed to assess the BPR in well-trained individuals during a cycle ergometer test with a particular focus on the systolic BP (SBP) and to investigate pulse arrival time (PAT) as a continuous surrogate for SBP during exercise testing. Materials and Methods: Eighteen well-trained male cyclists were included (32.4 ± 9.4 years; maximal oxygen uptake 63 ± 10 ml/min/kg) and performed a stepwise lactate threshold test with 5-minute stages, followed by a continuous test to voluntary exhaustion with 1-min increments when cycling on an ergometer. BP was measured with a standard automated exercise BP cuff. PAT was measured continuously with a non-invasive physiological measurements device (IsenseU) and metabolic consumption was measured continuously during both tests. Results: At lactate threshold (281 ± 56 W) and maximal intensity test (403 ± 61 W), SBP increased from resting values of 136 ± 9 mmHg to maximal values of 219 ± 21 mmHg and 231 ± 18 mmHg, respectively. Linear within-participant regression lines between PAT and SBP showed a mean r 2 of 0.81 ± 17. Conclusion: In the present study focusing on the BPR in well-trained individuals, we observed a more exaggerated systolic BPR than in comparable recent studies. Future research should follow up on these findings to clarify the clinical implications of the high BPR in well-trained individuals. In addition, PAT showed strong intra-individual associations, indicating potential use as a surrogate SBP measurement during exercise testing.

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

A multi-channel measurement system used to measure electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) biosignals has been designed and prototyped. The designed system has 16 configurable measurement channels. Of the 16 channels the developed system has, 8 have been designed for EEG, 2 for EMG, 2 for EOG, 1 for ECG measurements, the remaining 3 have been reserved as backup channels. In circuit design, biosignal amplifier design principles have been applied by taking into account the characteristics of the biosignal to be measured for each channel, such as bandwidth, frequency, amplitude, noise level. Modules such as instrumentation amplifier, filter, DC suppression unit, amplifier, DC level determination unit, analog-digital converter, optical isolation unit, power supply have been designed to perform biosignal measurements through these channels. Biosignals measured by the developed system can be shifted to the desired threshold level with the help of the analog output reference voltage, converted to digital data 10-bit resolution and transferred to the computer environment in real time. The data transferred to the computer can be used in C#, Excel, MATLAB, and LabVIEW platforms. The novelty of the developed system is that any of the four desired biosignal types can be measured from any channel. In addition, another feature of the system is that it can work with real-time data without being dependent on the databases serving for human-computer interface applications. In experimental studies with some researchers for the performance tests of the system, ECG, EEG, EMG and EOG signals have been recorded with different module configurations, and signal processing stages were carried out to be used for human-computer applications.

Biosignal Measurement using Switching Voltage Divider Electrode—Aiming for Practical Application from Basic Research—

Biosignal Measurement using Switching Voltage Divider Electrode—Aiming for Practical Application from Basic Research—

Research on Contactless Bio-Signal Measurement Technology for Improving Social Awareness of Individuals with Communication Challenges

Youth and adults with autism spectrum disorder have poor skills such as communication, qualitative interaction, and emotional expression resulting in low social awareness. In this paper, we propose and explore a contactless bio-signal measurement and functional contents for improving social awareness of individuals with communication challenges. We implemented four individual methods for collecting and analyzing the bio data of the individuals without requiring their attention: (1) heart rate, (2) respiration, (3) facial expression, and (4) interaction. The four techniques are all based on image data received and analyzed from a normal web camera. The data were analyzed in a real-time, fully functional algorithm: implementing the algorithm on a mobile device will require future work. However, we have evaluated our method by developing a functional content including the four methods. Based on the analysis of the collected data from the content and qualitative responses from the field, the contactless bio-signal measurement technology combined with friendly designed user interfaces for the individuals with communication challenges could train them to improve their social awareness.

Comparison of silver-plated nylon (Ag/PA66) e-textile and Ag/AgCl electrodes for bioelectrical impedance analysis (BIA)

Recently, researchers have adapted Bioelectrical Impedance Analysis (BIA) as a new approach to objectively monitor wounds. They have indicated various BIA parameters associated to specific wound types can be linked to wound healing through trend analysis relative to time. However, these studies are conducted using wet electrodes which have been identified as possessing several shortcomings, such as unstable measurements. Thus, the adaption of e-textile electrodes has become an area of interest in measuring biosignals. E-textile electrodes are known to possess a significantly large polarization impedance (Z p ) that potentially influences these biosignal measurements. In this study we aim to identify the suitability of e-textile electrodes to monitor wounds using BIA methodologies. By adapting suggested methodologies conducted in-vivo from previous studies, we used an ex-vivo model to observe the behaviour of e-textile electrodes relative to time. This was compared to common clinical wet electrodes, specifically Ag/AgCl. The objective of this study was to identify the BIA parameters that can be used to monitor wounds with e-textile electrodes. By analysing the BIA parameters relative to time, we observed the influence of Z p on these parameters.

Implementation of a Multimedia Computing Healthcare Service Using a Voice Signal-Analysis Module and IR-UWB

As the population ages and the number of people with chronic diseases increases, society is changing from the existing diagnostic- and treatment-oriented medical services to preventive- and management-oriented medical services. This paradigm is named Ubiquitous Smart Healthcare. Various multimedia-based healthcare services are becoming especially key to health management in the smart age. In other words, technology, research, and integration of multimedia healthcare services, such as video data, voice data, IoT networks, and the like, are proceeding. Due to well-being trends and lifestyles of the aging society, medical services are rapidly becoming ubiquitous, in which the individual and the medical professional cooperate closely in hospital-centered treatment services to provide health management and promotion, and prevention of diseases. To realize this, various bio-signal measurement techniques based on multimedia are being studied and applied. Therefore, in this paper, we propose a remote voice signal-measurement system that combines two Impulse Radio Ultra Wide Band (IR-UWB) systems to provide a capable IR real-time location service, and we design and implement them. Also, we can reduce the complexity of existing voice signal–measurement algorithms, and we optimize the system by proposing a filter suitable for voice signal–measurement algorithms. As a result, we confirm that the voice signal can be measured by extending the measurement distance (based on wireless communications) by only about 3 m to 10 m. By applying the measurement and analysis method for voice-signal data in the wireless communication system environment, a healthcare service with improved reliability can be applied. In particular, it is possible to provide a multimedia healthcare computing environment in which new diagnostic data can be found and utilized in the field of multimedia-based healthcare service technology.