Physiological Data Acquisition Research Articles

Multimodal decoding of error processing in a virtual reality flight simulation

Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only. We examined the feasibility of decoding errors solely with pupil size data and proposed a hybrid decoding approach combining electroencephalographic (EEG) and pupillometric signals. Moreover, we analyzed if hybrid approaches can improve existing EEG-based classification approaches and focused on setups that offer increased usability for practical applications, such as the presented game-like virtual reality flight simulation. Our results indicate that classifiers trained with pupil size data can decode errors above chance. Moreover, hybrid approaches yielded improved performance compared to EEG-based decoders in setups with a reduced number of channels, which is crucial for many out-of-the-lab scenarios. These findings contribute to the development of hybrid brain-computer interfaces, particularly in combination with wearable devices, which allow for easy acquisition of additional physiological data.

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

Challenges Related to Acquisition of Physiological Data for Physiologically Based Pharmacokinetic (PBPK) Models in Postpartum, Lactating Women and Breastfed Infants-A Contribution from the ConcePTION Project.

Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2-4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models.

PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy.

The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.

Exercise improves cardiac function in the aged rats with myocardial infarction.

Exercise can improve the cardiovascular health. However, the mechanism contributing to its beneficial effect on elderly patients with myocardial infarction is obscure. 20-month-old male Sprague-Dawley rats were used to establish myocardial infarction (MI) model by permanent ligation of the left anterior descending coronary artery (LAD) of the heart, followed by 4-week interval exercise training on a motor-driven rodent treadmill. The cardiac function, myocardial fibrosis, apoptosis, oxidative stress, and inflammatory responses were determined by using pressure transducer catheter, polygraph physiological data acquisition system, Masson's trichrome staining, and ELISA to evaluate the impact of post-MI exercise training on MI. Western blot were performed to detect the activation of AMPK/SIRT1/PGC-1alpha signaling in the hearts of aged rats. Exercise training significantly improved cardiac function and reduced the cardiac fibrosis. In infarcted heart, the apoptosis, oxidative stress, and inflammation were significantly reduced after 4-week exercise training. Mechanistically, AMPK/SIRT1/PGC-1alpha pathway was activated in the myocardial infarction area after exercise training, which might participate in the protection of cardiac function. Exercise training improves cardiac function in MI rats through reduction of apoptosis, oxidative stress, and inflammation, which may mediate by the activation of AMPK/SIRT1/PGC-1alpha signaling pathway.

Prognostic value of coronary flow reserve and coronary calcification in patients with suspected coronary artery disease

Abstract Introduction Current cardiac PET and computed tomography imaging allows the simultaneous acquisition of anatomic and physiological data for patients suspected of CAD. Purpose We aimed to evaluate the incremental prognostic value of coronary artery calcium (CAC) score and coronary flow reserve (CFR) in patients with suspected coronary artery disease (CAD) undergoing positron emission tomography (PET) myocardial perfusion imaging (MPI). Methods We included consecutive patients who underwent PET MPI and CAC score calculation at Houston Methodist Hospital between August 2019 and January 2022. MPI and CAC scores were obtained in the same setting. The primary endpoint of the study was MACE. Cox proportional hazard regression was used to assess the incremental prognostic value of CAC and CFR by sequentially adding the variables to a model that included clinical and PET variables. Results A total of 2,072 patients (mean age 65.7±11.6 years, 54.7% women) were included in the analysis. Risk factors were prevalent (86.1% hypertension, 75.2% dyslipidemia, 45.5% diabetes). In total, 25.3% of the cohort had a CAC score of 0, 38.1% had CAC ≥400, and 39.9% had CFR <2. Over a median follow up of 6.26 (IQR 1.7–12.4) months, 66 (3.2%) patients had cardiac death/nonfatal myocardial infarction. CFR score added incremental prognostic value over clinical and perfusion variables alone (base model: c-index 0.75; Akaike information criterion [AIC]: 1,860.6; p<0.001; CFR model: c-index 0.78; AIC: 1,842.4; p=0.041). However, CAC score did not show an increased prognostic utility (CAC model: c-index 0.76; AIC: 1,854.8; p=0.087 vs. base model), and neither did the combined model of CAC and CFR scores (c-index 0.79; AIC: 1,839.4; p=0.366 vs. CFR model). Conclusions Among patients referred for PET MPI, CFR score added an increased prognostic value over clinical and MPI variables. However, when evaluating the prognostic utility of CAC alone, or of CAC and CFR combined, the multivariable model did not show any increased prognostic potential. Funding Acknowledgement Type of funding sources: None.

Fatigue Detection Post Physical Activity Using Machine Learning Algorithms

Construction work is purely effortful and the prevention of injuries at construction job sites is essential for encouraging worker’s well being and health which is generally overlooked at the construction sites. World’s construction industry is one amongst those having unsatisfactory work health issues. A large number of laborers and construction workers have to undergo fatigue risk at their job place. This paper describes the current state of the art of the research carried out in case of fatigue assessment after performing some physical activity providing an insight into fatigue, its detection and an overview of the causes of risk fatigue and its countermeasures. A number of subjective and objective fatigue assessment approaches have been used that have further stimulated the inclusion of latest and advanced approaches for fatigue detection. Although individual’s knowledge regarding the fatigue detection approaches has been enhanced, there is as yet minimal research in the field of fatigue detection post physical activity. In this study the stress would be on objective fatigue detection techniques where the acquisition of physiological data of construction workers is required. The study provides a ground for detection of physiological fatigue based on motion capture data in the form of videos.

MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments

AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.

Computational lung modelling in respiratory medicine.

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.

Wearable Sensors for Vital Signs Measurement: A Survey

With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.

Study on body area network of smart clothing for physiological monitoring

With the popularity of the concept of big health and the important role of wearable devices in medical health, users pay more attention to the collection and acquisition of physiological data, but wearable devices attached to human users are independent, and the degree of data sharing is low. Improving the data sharing, accuracy, and reliability of wearable device monitoring is a problem that the article needs to study and solve. Specifically, the researcher summarizes the characteristics of the physiological monitoring smart clothing, and the basic physiological data parameters of human body, and analyzes the collection of three basic signals of electrocardiogram, body temperature, and human movement. This article summarizes the requirements and key technologies of body area network transmission of smart clothing, and studies the body area network node design, energy consumption optimization mode, and network architecture of physiological monitoring smart clothing. At the same time, based on the previous research, the multi-interaction process of smart clothing is formed, and then the standard evaluation system of smart clothing body domain network for evaluation is proposed. The results show that the optimized structure of the body area network of smart clothing proposed by the researchers is efficient, convenient, and mobile, and meets the characteristics of safety, reliability, low power consumption, and portability of smart clothing, especially in the field of physiological monitoring. The standard evaluation system of smart clothing body area network provides a practice-oriented theoretical reference for the current research of smart clothing body area network.

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

Brow and Masticatory Muscle Activity Senses Subjective Hedonic Experiences during Food Consumption.

Sensing subjective hedonic or emotional experiences during eating using physiological activity is practically and theoretically important. A recent psychophysiological study has reported that facial electromyography (EMG) measured from the corrugator supercilii muscles was negatively associated with hedonic ratings, including liking, wanting, and valence, during the consumption of solid foods. However, the study protocol prevented participants from natural mastication (crushing of food between the teeth) during physiological data acquisition, which could hide associations between hedonic experiences and masticatory muscle activity during natural eating. We investigated this issue by assessing participants’ subjective ratings (liking, wanting, valence, and arousal) and recording physiological measures, including EMG of the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles while they consumed gel-type solid foods (water-based gellan gum jellies) of diverse flavors. Ratings of liking, wanting, and valence were negatively correlated with corrugator supercilii EMG and positively correlated with masseter and suprahyoid EMG. These findings imply that subjective hedonic experiences during food consumption can be sensed using EMG signals from the brow and masticatory muscles.

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA.Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1

Autonomic nervous pattern analysis of trait anxiety

High trait anxiety (HTA) causes cognitive control deficit, implicit emotion regulation deficit and risks of emotional disorders. The current work designed a paradigm for autonomic nervous pattern analysis of trait anxiety. The paradigm includes three phases: (1) physiological data acquisition of autonomic nervous activity in resting baseline status, during the preparation and presentation of an evaluated speech, and in recovery status after stressful task; (2) feature extraction and selection of the physiological data; (3) pattern recognition of high/low trait anxiety through commonly used classifiers. By the operation of the above paradigm, we analyzed 99 samples of respiratory (Resp) data and RR interval series calculated from the ECG data of 99 subjects. We have found that: (1) the autonomic nervous activity of trait anxiety has obvious gender difference; (2) After eliminating gender difference by modeling of trait anxiety for each gender, the HTA and low trait anxiety (LTA) populations can be distinguished by the decision tree classifier with accuracies of 66.67% and 100% for females and males, respectively; (3) for the females, the critical two-dimension feature subset of HTA vs. LTA classification comes from the resting baseline status, stress recall status and stress withdrawal status; (4) for the males, the critical two-dimension feature subset of HTA vs. LTA classification comes from the resting baseline status and the stress withdrawal status. The results have shown that HTA and LTA have baseline autonomic nervous difference, and the stress reactions of autonomic nervous system of HTA and LTA populations are different for different genders.

Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

The percentage of people around the world who are living with some kind of disability or disorder has increased in recent years and continues to rise due to the aging of the population and the increase in chronic health disorders. People with disabilities find problems in performing some of the activities of daily life, such as working, attending school, or participating in social and recreational events. Neurological disorders such as epilepsy, learning disabilities, autism spectrum disorder, or Alzheimer’s, are among the main diseases that affect a large number of this population. However, thanks to the assistive technologies (AT), these people can improve their performance in some of the obstacles presented by their disorders. This paper presents a new system that aims to help people with neurological disorders providing useful information about their pathologies. This novelty system consists of a platform where the physiological and environmental data acquisition, the feature engineering, and the machine learning algorithms are combined to generate customs predictive models that help the user. Finally, to demonstrate the use of the system and the working methodology employed in the platform, a simple example case is presented. This example case carries out an experimentation that presents a user without neurological problems that shows the versatility of the platform and validates that it is possible to get useful information that can feed an intelligent algorithm.

Myocardial Flow Reserve and Coronary Calcification in Prognosis of Patients With Suspected Coronary Artery Disease

ObjectivesThe aim of this analysis is to examine the incremental prognostic value of coronary artery calcium (CAC) score and myocardial flow reserve (MFR) in patients with suspected coronary artery disease (CAD) undergoing positron emission tomography (PET) myocardial perfusion imaging (MPI). BackgroundAdvances in cardiac PET and computed tomography imaging enabled the simultaneous acquisition of anatomic and physiological data for patients suspected of CAD. MethodsConsecutive patients who underwent PET MPI and CAC score calculation at King Abdulaziz Cardiac Center, Riyadh, Saudi Arabia, between May 2011 and May 2018 were included in the study. MPI and CAC images were obtained in the same setting. The primary endpoint of the study was a composite of cardiac death and nonfatal myocardial infarction. Cox proportional hazard regression was used to assess the incremental prognostic value of CAC and MFR by sequentially adding the variables to a model that included clinical and PET variables. ResultsA total of 4,008 patients (mean age 59.7 ± 11.6 years, 55% women) were included in the analysis. Risk factors were prevalent (77.6% hypertension, 58.1% diabetes). In total, 35.9% of the cohort had CAC of 0, 16.5% had CAC ≥400, and 43.9% had MFR <2. Over a median follow up of 1.9 years, 130 (3.2%) patients had cardiac death/nonfatal myocardial infarction. CAC and MFR score added incremental prognostic value over clinical and perfusion variables (base model: c-index 0.8137; Akaike information criterion [AIC]: 1,865.877; p = 0.0011; CAC model: c-index = 0.8330; AIC: 1,850.810; p = 0.045 vs. base model; MFR model: c-index = 0.8279; AIC: 1,859.235; p = 0.024). Combining CAC and MFR did not enhance risk prediction (c-index = 0.8435; AIC: 1,846.334; p = 0.074 vs. MFR model; p = 0.21 vs. CAC model.) ConclusionsIn this large cohort of patients referred for PET MPI, both CAC and MFR independently added incremental prognostic value over clinical and MPI variables. Although combining both may have synergetic prognostic effect, this relation was not shown in multivariable model of this analysis.

Towards Continuous and Ambulatory Blood Pressure Monitoring: Methods for Efficient Data Acquisition for Pulse Transit Time Estimation.

We developed a prototype for measuring physiological data for pulse transit time (PTT) estimation that will be used for ambulatory blood pressure (BP) monitoring. The device is comprised of an embedded system with multimodal sensors that streams high-throughput data to a custom Android application. The primary focus of this paper is on the hardware–software codesign that we developed to address the challenges associated with reliably recording data over Bluetooth on a resource-constrained platform. In particular, we developed a lossless compression algorithm that is based on optimally selective Huffman coding and Huffman prefixed coding, which yields virtually identical compression ratios to the standard algorithm, but with a 67–99% reduction in the size of the compression tables. In addition, we developed a hybrid software–hardware flow control method to eliminate microcontroller (MCU) interrupt-latency related data loss when multi-byte packets are sent from the phone to the embedded system via a Bluetooth module at baud rates exceeding 115,200 bit/s. The empirical error rate obtained with the proposed method with the baud rate set to 460,800 bit/s was identically equal to . Our robust and computationally efficient physiological data acquisition system will enable field experiments that will drive the development of novel algorithms for PTT-based continuous BP monitoring.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework.

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

A Physiological-Monitoring Electronic Platform for Cattle Grazing Systems

In this work, we present a non-invasive electronic platform for physiological data acquisition on cattle grazing systems. The platform can be used for dairy and beef cattle to continuously monitor physiological variables such as skin temperature, heartbeats, and respiratory frequency. The set of sensors are coupled into a halter so that they are in touch with the animal's forehead. Users can monitor the data acquired by the electronic device using a mobile device (smartphone or tablet) and it visualizes important physiological parameters in the platform cloud system.