Real-time Physiological Data Research Articles

PHY Design and Implementation of a Galvanic Coupling Testbed for Intra-Body Communication Links

Intra-body communication (IBC) is a novel key research area that will foster personalized medicine by allowing in situ and real time monitoring in daily life. In this work, the energy efficient galvanic coupling (GC) technology is used to send data through intra-body links. A novel sound card-based GC testbed is designed and implemented, whose main features are: (i) low equipment requirements since it only employs two ordinary PCs with sound card support and Matlab software, (ii) high flexibility since all the parameters setting may be easily modified through the PC control panel and Matlab programs, (iii) real time physiological data transmissions, and (iv) almost error free communication by developing specific physical (PHY) layer techniques, which are implemented and tested with a real chicken tissue in the experimental evaluation. A signal to noise ratio (SNR) calculation is also proposed with the twofold purpose to be used for frequency offset compensation and as metric to evaluate the proposed architecture. The developed GC testbed may be easily replicated by the interested research community to carry out simulation-based experiments, thus fostering new research in this field. Moreover, the Matlab source code of the proposed GC transceiver is freely available online on Code Ocean.

Pulse Rate Variability in Emergency Physicians During Shifts: Pilot Cross-Sectional Study.

BackgroundThe high prevalence of physician burnout, particularly in emergency medicine, has garnered national attention in recent years. Objective means of measuring stress while at work can facilitate research into stress reduction interventions, and wearable photoplethysmography (PPG) technology has been proposed as a potential solution. However, the use of low-burden wearable biosensors to study training and clinical practice among emergency physicians (EP) remains untested.ObjectiveThis pilot study aimed to (1) determine the feasibility of recording on-shift photoplethysmographic data from EP, (2) assess the quality of these data, and (3) calculate standard pulse rate variability (PRV) metrics from the acquired dataset and examine patterns in these variables over the course of an academic year.MethodsA total of 21 EP wore PPG biosensors on their wrists during clinical work in the emergency department during a 9-hour shift. Recordings were collected during the first quarter of the academic year, then again during the fourth quarter of the same year for comparison. The overall rate of usable data collection per time was computed. Standard pulse rate (PR) and PRV metrics from these two time points were calculated and entered into Student t tests.ResultsMore than 400 hours of data were entered into these analyses. Interpretable data were captured during 8.54% of the total recording time overall. In the fourth quarter of the academic year compared with the first quarter, there was no significant difference in median PR (75.8 vs 76.8; P=.57), mean R-R interval (0.81 vs 0.80; P=.32), SD of R-R interval (0.11 vs 0.11; P=.93), root mean square of successive difference of R-R interval (0.81 vs 0.80; P=.96), low-frequency power (3.5×103 vs 3.4×103; P=.79), high-frequency power (8.5×103 vs 8.3×103; P=.91), or low-frequency to high-frequency ratio (0.42 vs 0.41; P=.43), respectively. Power estimates for each of these tests exceeded .90. A secondary analysis of the resident-only subgroup similarly showed no significant differences over time, despite power estimates greater than .80.ConclusionsAlthough the use of PPG biosensors to record real-time physiological data from EP while providing clinical care seems operationally feasible, this study fails to support the notion that such an approach can efficiently provide reliable estimates of metrics of interest. No significant differences in PR or PRV metrics were found at the end of the year compared with the beginning. Although these methods may offer useful applications to other domains, it may currently have limited utility in the contexts of physician training and wellness.

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring.

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6h; a unit is the fold-increase in risk of ICU transfer in the next 24h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

PEDIATRIC CARDIAC CRITICAL CARE OUTCOMES IMPROVE FOLLOWING IMPLEMENTATION OF A COMMERCIAL DATA AGGREGATION AND VISUALIZATION SOFTWARE PLATFORM

Software that aggregates and displays real-time physiologic data in the pediatric cardiac intensive care unit (CICU) holds promise to advance clinical care. We assessed multiple CICU outcomes at our institution before and after implementation of a commercial software platform and compared these

Remote Detection of Growth Dynamics in Red Lettuce Using a Novel Chlorophyll a Fluorometer

The production of food crops in controlled environment agriculture (CEA) can help mitigate food insecurity that may result from increasingly frequent and severe weather events in agricultural areas. Lighting is an absolute requirement for crop growth in CEA, and is undergoing rapid advances with the advent of tunable, light emitting diode (LED) systems. The integration of these systems into existing CEA environmental control architectures is in its infancy and would benefit from a non-invasive, rapid, real-time, remote sensor that could track crop growth under different lighting regimes. A newly-developed remote chlorophyll a fluorescence (ChlF) sensing device is described herein that provides direct, remote, real-time physiological data collection for integration into tunable LED lighting control systems, thereby enabling better control of crop growth and energy efficiency. Data collected by this device can be used to accurately model growth of red lettuce plants. In addition to monitoring growth, this system can predict relative growth rates (RGR), net assimilation rates (NAR), plant area (PA), and leaf area ratio (LAR).

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia.

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Safety Assessment of the Auto Manipulation Device for Acupuncture in Sprague‐Dawley Rats: Preclinical Evaluation of the Prototype

Background The Auto Manipulation Device for Acupuncture (AMDA) is designed for providing stable, quantified effects and higher frequency when doing lifting and thrusting manipulation. The purpose of this study is to investigate the safety of manipulation by AMDA in different frequency and duration in healthy rats. Methods The study was divided into two parts: single intervention and once a day for a week. 12 rats and 15 rats were randomly allocated to different groups: Control (needle insertion only), AMDA (2Hz/10Mins), AMDA (2Hz/20Mins), AMDA (20Hz/10Mins), and AMDA (20Hz/20Mins) for single and repeated interventions. Real-time physiological functions, laboratory data, and the bilateral muscle tissue of acupoint (ST 36) were obtained after the intervention. Results We found neither real-time physiological functions nor laboratory data differences between control group and AMDA groups in both parts. In the muscle tissue samples, the slight damage had been observed in the AMDA group with a frequency of 2 Hz for 20 minutes after once intervention, and the repeated session groups noted more obvious tissue damage with fibrotic change. Although the period was shorter, higher frequency manipulation caused more damage that fibroblast nuclei became more slender and obvious. However, no significant adverse effect was noted such as crippled and molting in the whole process. Conclusion Our study suggested that the safety issue of AMDA operation in rats is feasible because there was no difference between control group and AMDA groups among real-time physiological functions and laboratory data. However, manipulation with higher frequency should be more preserved.

A smartphone-based wearable sensors for monitoring real-time physiological data

Recently, Wireless Body Sensor Networks (WBSNs) have been popularly employed to measure people's physiological parameters, particularly for disease monitoring, prevention, and treatment. In this study, we propose a smartphone-based WBSN, named Mobile Physiological Sensor System (MoPSS), which collects users’ physiological data with body sensors embedded in a smart shirt. A patient's vital signs are continuously gathered and sent to a smart phone in a real-time manner. The data are then delivered to a remote healthcare cloud via WiFi. After performing necessary classification and analysis, the health information of individual patients is also stored in the cloud, from which authorized medical staffs can retrieve required data to monitor patients’ health conditions so that when necessary, caregivers are able to reach the patients as soon as possible and provide required assistance. Our simulations demonstrate that the presented healthcare system provides a better solution for health management.

On the Correlation Between Heart Rate and Driving Style in Real Driving Scenarios

Driving safety is of utmost importance in our society. The number of fatalities due to car accidents is still very high, and reducing this trend requires as much attention as possible. There are situations where the emotional conditions of drivers vary due to reasons beyond their control, or because they decide to change their driving style. Hence, we consider that such frequent situations deserve more scrutiny. In this work we addressed such issues by designing an Android application able to monitor in real-time both physiological data from the driver and diagnostic data from the vehicle (this data is obtained using an OBD-II connector) to study their correlation. Among the various non-invasive biomedical sensors available nowadays, we have adopted heart rate sensors, either packaged in belts or in smart watches. This allows studying the relationship between driving aggressiveness and heart rate. For our analysis we focused on fourteen different routes accounting a total driving time of 6 hours and 2 minutes, which we have split into three separate categories: urban, suburban, and highway routes. We analyzed the correlation between the heart rate and the driving style for each of the three groups. Our experiments show that the differences in terms of heart rate between quiet and aggressive behavior range between 2.5 % and 3 % beats per minute higher for the latter behaviour compared to the former.

Characterization of Impulse Radio Intrabody Communication System for Wireless Body Area Networks

Intrabody communication (IBC) is a promising data communication technique for body area networks. This short-distance communication approach uses human body tissue as the medium of signal propagation. IBC is defined as one of the physical layers for the new IEEE 802.15.6 or wireless body area network (WBAN) standard, which can provide a suitable data rate for real-time physiological data communication while consuming lower power compared to that of radio-frequency protocols such as Bluetooth. In this paper, impulse radio (IR) IBC (IR-IBC) is examined using a field-programmable gate array (FPGA) implementation of an IBC system. A carrier-free pulse position modulation (PPM) scheme is implemented using an IBC transmitter in an FPGA board. PPM is a modulation technique that uses time-based pulse characteristics to encode data based on IR concepts. The transmission performance of the scheme was evaluated through signal propagation measurements of the human arm using 4- and 8-PPM transmitters, respectively. 4 or 8 is the number of symbols during modulations. It was found that the received signal-to-noise ratio (SNR) decreases approximately 8.0 dB for a range of arm distances (5–50 cm) between the transmitter and receiver electrodes with constant noise power and various signal amplitudes. The SNR for the 4-PPM scheme is approximately 2 dB higher than that for the 8-PPM one. In addition, the bit error rate (BER) is theoretically analyzed for the human body channel with additive white Gaussian noise. The 4- and 8-PPM IBC systems have average BER values of 10−5 and 10−10, respectively. The results indicate the superiority of the 8-PPM scheme compared to the 4-PPM one when implementing the IBC system. The performance evaluation of the proposed IBC system will improve further IBC transceiver design.

A Cyber-Physical System for Product Conceptual Design Based on an Intelligent Psycho-Physiological Approach

Conceptual design plays an important role in a product development process. Significant development of technologies in the cyber-physical system (CPS) provides innovative approaches for product conceptual design. This paper proposes an architecture of CPS for conceptual design that realizes the acquisition of real-time physiological data from the physical world and the feedback of psychological states from the cyber world. As understanding and meeting the needs of customers have been recognized as significant aspects for conceptual design, an intelligent psycho-physiological approach that incorporates electroencephalogram (EEG) into the Kano model is adopted in this CPS for real-time customer needs analysis. The sample entropy (SampEn) extracted from EEG data is an endogenous neural indicator for customers’ psychological states. A support vector machine using this SampEn as input is trained for classifying different categories of quality attributes defined in the Kano model. A case study is conducted to testify the feasibility of the approach proposed in this paper.

Enabling real-time information service on telehealth system over cloud-based big data platform

We design a data coherence protocol for the PHR-based distributed system.We propose a flow estimating algorithm for the telehealth cloud system.We apply several predicting methods for the future bandwidth consumption.We present a telehealth framework for bandwidth balance on emergency. A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems do not consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients' information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.

Generic processing of real-time physiological data in the cloud

There is an emerging market in the collection of physiological data for analysis and presentation to end-users via web technologies for applications including health and fitness, telemedicine and self-tracking. As technology has improved, real-time streaming of physiological data, providing end-to-end user feedback has become feasible, allowing for innovative applications to be developed. Currently, there is no standardised method of collecting physiological data over the web for analysis and feedback to an end-user in real-time; existing platforms only support specific devices and application domains. This paper proposes a generic methodology and architecture for the collection, analysis and presentation of physiological data. It defines a standard method of encapsulating data from heterogeneous sensors, performing transformations on it and analysing it. The approach is evaluated through an implementation of the architecture using cloud computing technologies and an appropriate case study.

Generic processing of real-time physiological data in the cloud

Generic processing of real-time physiological data in the cloud

VConnect: perceive and interact with real world from CAVE

The Cave Automatic Virtual Environment (CAVE) is a fully immersive Virtual Reality (VR) system. CAVE systems have been widely used in many applications, such as architectural and industrial design, medical training and surgery plan, museums and education. However, one limitation for most of the current CAVE systems is that they are separated from the real world. The user in the CAVE is not able to sense the real world around him or her. In this paper, we propose a vConnect architecture, which aims to establish real-time bidirectional information exchange between the virtual world and the real world by utilizing the advanced technologies in cloud computing, mobile communications, wireless sensor networks, and computer vision. Specifically, we address three technical challenges in the proposed vConnect architecture. First, we propose an optimal allocation scheme for the wireless sensor networks to ensure that the data streams captured by the sensors can be delivered to the cloud servers in a reliable and prompt way. Second, we optimize the allocation of the cloud resources to ensure that the data streams sent from the clients can be processed promptly by the cloud servers at a minimal resource cost. Third, we propose to use marker-based finger interactions such that the user in the CAVE can manipulate the information in a natural and intuitive way. Fourth, we implemented a vHealth prototype, a CAVE-based real-time health monitoring system, to validate the proposed vConnect architecture. We demonstrated in the vHealth prototype that the user in the CAVE can visualize and manipulate the real-time physiological data of the patient who is being monitored, and interact with the patient.

Experimental UWB frequency analysis for implant communications.

Implantable biomedical sensors with the ability to transmit wirelessly real-time physiological data to an external unit can enable better management of chronic diseases. The IEEE Standard 802.15.6-2012 specifies the implementation of implant communications within 402-405 MHz, which unfortunately allows low data transmission rates only. Ultra wideband (UWB) interfaces within 3.1-10.6 GHz offer a number of advantages at the expense of higher path losses. Efforts to characterize the implant UWB channel have been undertaken via computer simulations, but these may not capture completely the effects on the implant radio channel of multiple physiological functions. To overcome these limitations we provide insight into the frequency-domain behavior of the UWB implant channel within 3.1-8.5 GHz based on propagation measurements in a liquid phantom and a living swine. A thorough comparison of the relative received power in phantom-based and in vivo measurements for the in-body to on-body (IB2OB) and in-body to off-body (IB2OFF) channel scenarios are presented.

Personalized Monitors for Real-Time Detection of Physiological States

The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.

Smart sensor: a platform for an interactive human physiological state recognition study

This paper describes a concept of making interactive human state recognition systems based on smart sensor design. The token measures on proper ADC signal processing had significantly lowered the interference level. A more reliable way of measuring human skin temperature was offered by using Maxim DS18B20 digital thermometers. They introduced a more sensible response to temperature changes compared to previously used analog LM35 thermometers. An adaptive HR measuring algorithm was introduced to suppress incorrect ECG signal readings caused by human muscular activities. User friendly interactive interface for touch sensitive GLCD screen was developed to present real time physiological data readings both in numerals and graphics. User was granted an ability to dynamically customize data processing methods according to his needs. Specific procedures were developed to simplify physiological state recording for further analysis. The introduced physiological data sampling and preprocessing platform was optimized to be compatible with “ATmega Oscilloscope” PC data collecting and visualizing software.

1618 Identification of Noxious Events for Newborn Infants with a Neural Network

BackgroundRecognition of pain experienced by immature and/or critically ill newborn infants in the Neonatal Intensive Care Unit remains a challenge despite the use of objective scoring systems that depend on...

Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals

Quantifying stress levels of an individual based on a mathematical analysis of real-time physiological data measurements is challenging. This study suggests a stochastic fuzzy analysis method to evaluate the short time series of R-R intervals (time intervals between consecutive heart beats) for a quantification of the stress level. The 5-min-long series of R-R intervals recorded under a given stress level are modeled by a stochastic fuzzy system. The stochastic model of heartbeat intervals is individual specific and corresponds to a particular stress level. Once the different heartbeat interval models are available for an individual, an analysis of the given R-R interval series generated under an unknown stress level is performed by a stochastic interpolation of the models. The stress estimation method has been implemented in a mobile telemedical application employing an e-health system for an efficient and cost-effective monitoring of patients while at home or at work. The experiments involve 50 individuals whose stress scores were assessed at different times of the day. The subjective rating scores showed a high correlation with the values predicted by the proposed analysis method.