Biosignals - Science topic

Objective: To address an emerging need for large amount of diverse datasets for proper training of artificial intelligence (AI) algorithms and for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram (EGG) time series. Methods: We used EGG data from an open database to...

This Ph.D. thesis is an attempt to apply ML approaches to selected problems of solid state physics and biophysics. The first part is devoted to phase transitions in physical models - the problem that has been known and studied from the beginning of condensed matter and statistical physics. Despite many efforts that have been put into defining indic...

The link between the cortical and cardiovascular systems is garnering increased attention due to its potential to offer valuable insights into brain and heart function coupling. Current joint analysis methodologies largely involve invasive or high-cost neuroimaging methods. EEG and ECG/PPG, however, provide non-invasive, cost-effective, and portabl...

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair...

Hydrogels offer tissue-like softness, stretchability, fracture toughness, ionic conductivity, and compatibility with biological tissues, which make them promising candidates for fabricating flexible bioelectronics. A soft hydrogel film offers an ideal interface to directly bridge thin-film electronics with the soft tissues. However, it remains diff...

The reconstruction of biomedical signals from noisy measurements has been an indispensable research topic. A majority of biosignals exhibit typical piecewise characteristics. The recovery of these piecewise biomedical signals embedded in noise through conventional nonlinear filtering schemes fails due to the lack of proper balance between strict sp...

To make machines emotionally more intelligent, it is necessary to choose the right measuring devices that are able to collect physiological biosignals, which can be used to recognize the emotional state of the user. These devices should not restrict the users in their everyday life, therefore commercial, non-invasive Internet of Things (IoT) device...

Objective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. Study design: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligibl...

The chapter presents and discusses the theoretical background, original methodology, format, and results of the workshop “Interdisciplinary challenge on Emerging Materials and Technologies (EM&Ts)” with a focus on Interactive Connected and Smart (ICS) Materials for Wearable Technologies. ICS materials are defined as systems combining inactive mater...

The aim of this work is to develop and evaluate a multi-stage procedure model for the identification of use problems and optimization of usability using biosignal data. The concept is divided into 5 steps: 1. static analysis of data to identify use problems; 2. conducting interviews within the context of use and requirements analysis to investigate...

Due to recent developments in wearable sensor technology, textile electrodes are routinely being employed in electromyography (EMG) for continuous monitoring of the biosignals from the muscles. However, the performance of such smart textile-based health monitoring devices depends on several factors such as, the sensitivity (impedance), durability (...

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more com...

Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals are typically specialized for specific datasets and clinical settings, limiting their broader applicability. Motivated by the success of lar...

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, wh...

The facial and physiological sensor-based emotion recognition methods are two popular methods of emotion recognition. The proposed research is the frst of its kind in real-time emotion recognition that combines skin conductance signals with the visual-based facial emotion recognition (FER) method on a Raspberry Pi. This research includes stepwi...

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 metho...

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is t...

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of informatio...

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linkin...

In an effort to make biosignal integration more accessible to explore for more HCI researchers, this paper presents our investigation of how well a standard, near ubiquitous webcam can support remote sensing of heart rate and respiration rate across skin tone ranges. The work contributes: how the webcam can be used for this purpose, its limitations...

Currently, numerous monoelemental two-dimensional (2D) materials, called Xenes, have been discovered, including graphyne (GD), silicene, germanene, arsenene, and borophene. Their structures, fabrication methods, as well as properties have been extensively explored. Based on their single-element composition, high optical response capability, excelle...

Using biosignals through electromyography (EMG) and rendering them as feedback for hands-free interaction finally migrates to engaging virtual reality (VR) experiences for health and fitness-related applications. Previous work proposes various body locations as input sources and different output modalities for creating effective biofeedback loops....

Brain‐computer interface applications for biometric person identification have increased their interest in recent years since they are potentially more secure and more difficult to counterfeit than traditional biometric techniques. However, it is necessary to consider how brain waves are acquired for this purpose, not only in terms of efficiency bu...

In search of missing tactile sensations in telepresent virtual reality (VR), we set of to explore how we might be able to apply pseudo-haptics to elicit an embodied illusion of social touch. We developed ETC (Embodied Telepresent Connection), a prototype that allows two participants to interact remotely through pseudo-haptic touch via their abstrac...

Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved proble...

Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of tr...

p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedi...

The PEDOT polymer electrode is a metal-free electrode, consisting of an acrylate (dental composite) and the conductive polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The electrode is applied as gel onto the skin and cured with blue light for 10–20 s in order to achieve a conductive bond to the skin. The electrodes are u...

Background and Objective: The automatic assessment of pain is vital in designing optimal pain management interventions focused on reducing suffering and preventing the functional decline of patients. In recent years, there has been a surge in the adoption of deep learning algorithms by researchers attempting to encode the multidimensional nature of...

p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedi...

p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedi...

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user’s comfort. In this study, we have designed and realized a novel weara...

Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. Multicultural influences and societal conditions are also crucial to consider from a global perspective. Applying neuroscience tools and techniques in international marketing and consumer behavior is an em...

Aim Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design consider...

Background: Independent Component Analysis (ICA) is the most common and standard technique used in functional neuroscience data analysis. Objective: In this study, two of the significant functional brain techniques are introduced as a model for neuroscience data analysis. Material and methods: In this experimental and analytical study, Electro...

Background: This study explored the physiological correlates of students' self-reported emotions in ecologically valid settings by combining biosignal data (on physical and cardiac activity) and experience sampling method (ESM) data. Aims: The aim was to examine the concurrent associations between self-reported excitement, calmness, anxiety, and...

The paper proposes a unsupervised method for detecting the three stages of sleep—wake, rapid eye movement (REM) sleep, and non-REM sleep—using biosignals obtained from a 61 GHz single frequency modulated continuous wave (FMCW) radar. To detect the subject’s sleep stages based on non-learning techniques, the breathing and movement information charac...

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal...

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states m...

Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more powe...

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO2, temperature, and energy expenditure during act...

Low-cost sensors and single circuit boards such as Arduino and Raspberry Pi have increased the possibility of measuring biosignals by smart textiles with embedded electronics. One of the main problems with such e-textiles is their washability. While batteries are usually removed before washing, single-board computers and microcontrollers, as well a...

Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to Neurological and allied sciences. This system measures the activity of the Central Nervous System (CNS) using biosignals and output...

Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In t...

Despite their immense success in numerous fields, machine and deep learning systems have not have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their performance deteriorates...

Background: In the clinical context, the assessment of pain in patients with inadequate communication skills is standardly performed externally by trained medical staff. Automated pain recognition (APR) could make a significant contribution here. Hereby, pain responses are captured using mainly video cams and biosignal sensors. Primary, the automa...

Poetry elicits emotions, and emotion is a fundamental component of human ontogeny. Although neuroaesthetics is a rapidly developing field of research, few studies focus on poetry, and none address its different modalities of fruition (MOF) of universal cultural heritage works, such as the Divina Commedia (DC) poem. Moreover, alexithymia (AX) result...

Digital video is a crucial component of multimedia that enhances presentations with accurate, engaging visual and aural data that affects several industries. The transition of video storage from analog to digital is being fueled by a variety of causes. Improved compression methods, cheaper technology, and more network needs are some of these driver...

Background Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. Methods To compare as ma...

Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to automatically classify the quality of ECG data. A total of 31,127 twenty-s ECG segments of 250 Hz wer...

Stress is now thought to be a major cause to a wide range of human health issues. However, many people may ignore their stress feelings and disregard to take action before serious physiological and mental disorders take place. The heart rate (HR) and blood pressure (BP) are the most physiological markers used in various studies to detect mental str...

Stress is now thought to be a major cause to a wide range of human health issues. However, many people may ignore their stress feelings and disregard to take action before serious physiological and mental disorders take place. The heart rate (HR) and blood pressure (BP) are the most physiological markers used in various studies to detect mental str...

Estimation of motion intention of human hand has many applications in robotics and other human centred areas. Especially with wearable robotic applications biosignal based estimation of human hand motions are widely used. However, based on the construction of the muscles for finger motions, and the higher number of independent motions of the human...

Health research and health care alike are presently based on infrequent assessments that provide an incomplete picture of clinical functioning. Consequently, opportunities to identify and prevent health events before they occur are missed. New health technologies are addressing these critical issues by enabling the continual monitoring of health-re...

3D printed on‐skin electrodes are of notable interest because, unlike traditional wet silver/silver chloride (Ag/AgCl) on‐skin electrodes, they can be personalized and 3D printed using a variety of materials with distinct properties such as stretchability, conformal interfaces with skin, biocompatibility, wearable comfort, and, finally, low‐cost ma...

Background: Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia. Methods: This...

Feature attribution methods identify which features of an input most influence a model's output. Most widely-used feature attribution methods (such as SHAP, LIME, and Grad-CAM) are "class-dependent" methods in that they generate a feature attribution vector as a function of class. In this work, we demonstrate that class-dependent methods can "leak"...

Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration...

With the increasing number of devices that we have available to monitor our health, the conditions in which the measurements are done are no longercontrolled environments. Apart from all the motion artifacts that may affect the biosignal, the acquisition system itself, being an electronic equipment, issubject to electromagnetic interferences which...

Applying quantum technology to dispatch face-to-face medical activities has generated significant interest. Unfortunately, the work on remote medical treatment soliciting quantum medication and information processing techniques is hard to observe. In this research, we proposed the Mach–Zehnder interferometer (MZI) based optoplasmonic biosensors (OP...

Entropy is a fundamental concept in the field of information theory. During measurement, conventional entropy measures are susceptible to length and amplitude changes in time series. A new entropy metric, neural network entropy (NNetEn), has been developed to overcome these limitations. NNetEn entropy is computed using a modified LogNNet neural net...

Although the effects of running on plantar pressures have been detailed on several surfaces with different hardness, there is a lack of studies assessing the mechanical behavior analysis by in-shoe plantar pressure sensors on different hardness mats during running. The aim of the present study was to determine in-shoe maximum forces and peak planta...