Physiological Signal Monitoring Research Articles

Multifunctional Fibers to Shape Future Biomedical Devices

AbstractFiber‐based configurations are highly desirable for wearable and implantable biomedical devices due to their unique properties, such as ultra‐flexibility, weavability, minimal invasiveness, and tissue adaptability. Recent developments have focused on the fabrication of fibrous devices with multiple biomedical functions, such as noninvasively or minimally invasively monitoring of physiological signals, delivering drugs, transplanting cells, and recording and stimulating nerves. In this Review, the recent progress of these multifunctional fiber‐based devices in terms of their composite materials, fabrication techniques, structural designs, device‐tissue interfaces, and biomedical applications is carefully described. The remaining challenges and future directions in this emerging and exciting research field are also highlighted.

Physiological Signal Preserving Video Compression for Remote Photoplethysmography

The consumer-level digital camera has become a physiological signal monitoring sensor due to the rapid growth of the remote photoplethysmography (rPPG) technique. However, rPPG suffers from the artifacts caused by video compression, a technique that widely exists in nearly all video-related applications. This limitation greatly narrows the application range of the rPPG. In this paper, a novel physiological signal preserving video compression algorithm called POSSC is proposed such that the existing rPPG signal extraction approaches can be applied directly on the compressed video without modification. The proposed approach consists of three main steps: 1) rPPG feature extraction; 2) sparse subspace clustering; and 3) region of interest (ROI)-based video compression. This paper models the skin/non-skin feature classification problem as sparse subspace clustering. Physiological signals are preserved by allocating more (fewer) bits to the ROI (non-ROI) regions. A self-collected benchmark dataset is established to evaluate the performance of POSSC in terms of body part, ROI size, light source, illumination intensity, and multiple subjects in an image. The results demonstrate that POSCC is effective in preserving physiological signals for facial videos under normal light intensity, insensitive to ROI size, shape, and the number of subjects. This paper also demonstrates the effectiveness of sparse subspace clustering for pulse region detection.

GEP-based predictive modeling of breathing resistances of wearing respirators on human body via sEMG and RSP sensors

PurposeBreathing resistance is the main factor that influences the wearing comfort of respirators. This paper aims to demonstrate the feasibility of using the gene expression programming (GEP) for the purpose of predicting subjective perceptions of breathing resistances of wearing respirators via surface electromyography (sEMG) and respiratory signals (RSP) sensors.Design/methodology/approachThe authors developed a physiological signal monitoring system with a specific garment. The inputs included seven physical measures extracted from (RSP) and (sEMG) signals. The output was the subjective index of breathing resistances of wearing respirators derived from the category partitioning-100 scale with proven levels of reliability and validity. The prediction model was developed and validated using data collected from 30 subjects and 24 test combinations (12 respirator conditions × 2 motion conditions). The subjects evaluated 24 conditions of breathing resistances in repeated measures fashion.FindingsThe results show that the GEP model can provide good prediction performance (R2 = 0.71, RMSE = 0.11). This study demonstrates that subjective perceptions of breathing resistance of wearing respirators on the human body can be predicted using the GEP via sEMG and RSP in real-time, at little cost, non-invasively and automatically.Originality/valueThis is the first paper suggesting that subjective perceptions of subjective breathing resistances can be predicted from sEMG and RSP sensors using a GEP model, which will remain helpful to the scientific community to start further human-centered research work and product development using wearable biosensors and evolutionary algorithms.

Characterization of a low consumption wireless sensor node for the intensive transmission of physiological signals

<span>This paper describes the development and implementation of low power consumption wireless sensor nodes for the periodic monitoring of physiological signals with intensive data transmission, using Wi-Fi and ZigBee wireless communication modules, obtaining operation characteristics from the energy point of view that allow to increase the life time of the sensor node. The sensor nodes are designed and built using low energy consumption electronic devices to evaluate their energy performance using current data, transmission time, data transmission period and the relationship with the sensor node's lifetime when transmitting electrocardiographic (ECG), temperature and pulse type physiological signals. The development of this work generates recommendations for the design, development and construction of sensor nodes where the energy consumption of the wireless communication modules is evaluated. In this way, results are obtained that can allow the data transmission period, current consumption and size of data sent to be related to the operating time, defining the operating conditions and wireless technologies that allow the optimization of energy consumption when data is sent to Internet monitoring applications.</span>

Flexible noncontact electrodes for comfortable monitoring of physiological signals

SummaryPhysiological signals such as electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) could objectively reflect the functioning status of the human body and the monitoring of these signals is useful for various applications including brain computer interface, neurological rehabilitation, and long‐term healthcare monitoring. Currently, wet electrodes are commonly used for the monitoring of physiological signals and it usually requires conductive gels to achieve high quality recordings, which may cause discomfort to the patient and increase risk of skin allergy. In this study, a noncontact electrode made of a multilayer flexible printed circuit without any rigid electronic components on either side was proposed. The flexible noncontact electrode was capable of measuring physiological signals without any direct skin contact or conductive gels and could be bent freely according to the local shape to achieve optimal capacitive coupling with the skin surface. The results showed that the proposed flexible noncontact electrode could obtain different physiological signals with good quality compared with traditional wet electrodes. The ECG signals could be reliably measured with different insulation materials between the skin and the electrode, with up to five layers of insulation materials. It was also found that flexible electrode could achieve higher signal‐to‐noise ratio and therefore had better performance than traditional hard printed circuit board electrode, when measuring EMG signal through the cloth and EEG signals over the hair. The proposed method of this study might provide a novel and comfortable way to measure physiological signals for neurological rehabilitation, wearable devices, and other healthcare applications.

Wearable and Flexible Textile Electrodes for Biopotential Signal Monitoring: A review

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study presents a systematic review of wearable textile electrodes in physiological signal monitoring, with discussions on the manufacturing of conductive textiles, metrics to assess their performance as electrodes, and an investigation of their application in the acquisition of critical biopotential signals for routine monitoring, assessment, and exploitation of cardiac (electrocardiography, ECG), neural (electroencephalography, EEG), muscular (electromyography, EMG), and ocular (electrooculography, EOG) functions.

Nanofiber-Reinforced Silver Nanowires Network as a Robust, Ultrathin, and Conformable Epidermal Electrode for Ambulatory Monitoring of Physiological Signals.

Extremely soft and thin electrodes with high skin conformability have potential applications in wearable devices for personal healthcare. Here, a submicrometer thick, highly robust, and conformable nanonetwork epidermal electrode (NEE) is reported. Electrospinning of polyamide nanofibers and electrospraying of silver nanowires are simultaneously performed to form a homogeneously convoluted network in a nonwoven way. For a 125 nm thick NEE, a low sheet resistance of ≈4 Ω sq-1 with an optical transmittance of ≈82% is achieved. Due to the nanofiber-based scaffold that undertakes most of the stress during deformation, the electric resistance of the NEE shows very little variation; less than 1.2% after 50 000 bending cycles. The NEE can form a fully conformal contact to human skin without additional adhesives, and the NEE shows a contact impedance that is over 50% lower than what is found in commercial gel electrodes. Due to conformal contact even under deformation, the NEE proves to be a stable, robust, and comfortable approach for measuring electrocardiogram signals, especially when a subject is in motion. These features make the NEE promising for use in the ambulatory measurement of physiological signals for healthcare applications.

Monitoring of physiological body signals and human activity based on ultra-sensitive tactile sensor and artificial electronic skin by direct growth of ZnSnO3 on silica cloth

This report demonstrates of the direct growth of ternary compound ZnSnO3 nanocubes on silica cloth for the fabrication of scalable tactile sensors for monitoring physiological signals which even the highly sensitive tactile sensors fail to measure and artificial electronic skin (E-skin) without the use of sophisticated silicon wafer based technologies. The fabricated sensor exhibited a sensitivity and response time of 0.12 KPa−1 and 350 ms respectively. Inspired by the inherent alignment of the ZnSnO3 nanocubes on silica microfibers, the fabricated sensor was further demonstrated to be able to recognize wrist pulse pressure, finger movement, wrist movement and finger pressing. Furthermore, large area sensors array (4 × 4) were fabricated and integrated onto human hand which can “feel” the touch and spatially map the response paving way for the development of E-skin. Also, the sensor array was able to detect and map the shape and location of unknown objects placed on it and could trace the index finger trajectory made on the sensor array. The fabricated pressure sensor was tested for its reliability by performing bending test studies under harsh deformations and 500 bending cycles which resulted in negligible change in the performance of the sensor.

Research on Non-Contact Monitoring System for Human Physiological Signal and Body Movement.

With the rapid increase in the development of miniaturized sensors and embedded devices for vital signs monitoring, personal physiological signal monitoring devices are becoming popular. However, physiological monitoring devices which are worn on the body normally affect the daily activities of people. This problem can be avoided by using a non-contact measuring device like the Doppler radar system, which is more convenient, is private compared to video monitoring, infrared monitoring and other non-contact methods. Additionally real-time physiological monitoring with the Doppler radar system can also obtain signal changes caused by motion changes. As a result, the Doppler radar system not only obtains the information of respiratory and cardiac signals, but also obtains information about body movement. The relevant RF technology could eliminate some interference from body motion with a small amplitude. However, the motion recognition method can also be used to classify related body motion signals. In this paper, a vital sign and body movement monitoring system worked at 2.4 GHz was proposed. It can measure various physiological signs of the human body in a non-contact manner. The accuracy of the non-contact physiological signal monitoring system was analyzed. First, the working distance of the system was tested. Then, the algorithm of mining collective motion signal was classified, and the accuracy was 88%, which could be further improved in the system. In addition, the mean absolute error values of heart rate and respiratory rate were 0.8 beats/min and 3.5 beats/min, respectively, and the reliability of the system was verified by comparing the respiratory waveforms with the contact equipment at different distances.

An FPGA implementation of the matching pursuit algorithm for a compressed sensing enabled e-Health monitoring platform

Wireless monitoring of physiological signals is an evolving direction in personalized medicine and home-based e-Health systems. There are several constraints in designing such systems, with two of the most important being energy consumption and data compression. Compressed Sensing (CS) is an emerging data compression technique that can be used to overcome those constraints. This work presents a low-complexity CS hardware implementation on a Field-Programmable Gate Array (FPGA) for the reconstruction of compressively sensed signals using the matching pursuit (MP) algorithm, targeting health-care applications. The proposed hardware design is based on pipeline optimization of the Programmable Logic (PL) implementation performed on the Zynq FPGA, which provides a significant performance enhancement, namely an increased processing speed and a reduced computational time since it is 115x faster than the Matlab implementation and 75x faster than the Processing System (PS) implementation carried out on the same Zynq FPGA device, while achieving alternative a high-quality signal recovery with a Peak Signal to Noise Ratio (PSNR) of 23.8 dB. Comparisons against other state-of-the-art methods showed that the low complexity of the MP algorithm can be exploited for providing almost similar results to more complex algorithms using 87–583 less Digital Signal Processor (DSP) cores, 28–540 less Block RAMs and 10,300 to 84,700 less Look-Up Table (LUT) slices.

An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals.

Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.

Stretchable Piezoelectric Sensing Systems for Self‐Powered and Wireless Health Monitoring

AbstractContinuous monitoring of human physiological signals is critical to managing personal healthcare by early detection of health disorders. Wearable and implantable devices are attracting growing attention as they show great potential for real‐time recording of physiological conditions and body motions. Conventional piezoelectric sensors have the advantage of potentially being self‐powered, but have limitations due to their intrinsic lack of stretchability. Herein, a kirigami approach to realize a novel stretchable strain sensor is introduced through a network of cut patterns in a piezoelectric thin film, exploiting the anisotropic and local bending that the patterns induce. The resulting pattern simultaneously enhances the electrical performance of the film and its stretchability while retaining the mechanical integrity of the underlying materials. The power output is enhanced from the mechano‐electric piezoelectric sensing effect by introducing an intersegment, through‐plane, electrode pattern. By additionally integrating wireless electronics, this sensing network could work in an entirely battery‐free mode. The kirigami stretchable piezoelectric sensor is demonstrated in cardiac monitoring and wearable body tracking applications. The integrated soft, stretchable, and biocompatible sensor demonstrates excellent in vitro and ex vivo performances and provides insights for the potential use in myriad biomedical and wearable health monitoring applications.

High sensitivity knitted fabric bi-directional pressure sensor based on conductive blended yarn

Flexible pressure sensors have a broad range of applications, such as physiological signal monitoring, human-machine interfacing devices and soft robotics. The main challenges are laborious fabrication process, complex structure, poor conforming to human body. In this study, we report a wearable pressure sensor based on conductive blended yarn that is made into a plain knit fabric construction. The knitted fabric can be used as both wale direction sensor and course direction sensor with different sensitivities. The underlying sensing mechanisms for the two directions are analyzed using equivalent circuit networks. The pressure sensor demonstrates high sensitivity (0.73 kPa−1) for a wide range of pressure from nearly zero to 100 kPa and a very short response time (less than 0.4 s).

A review of big data applications of physiological signal data.

The proliferation of smart physiological signal monitoring sensors, combined with the advancement of telemetry and intelligent communication systems, has led to an explosion in healthcare data in the past few years. Additionally, access to cheaper and more effective power and storage mechanisms has significantly increased the availability of healthcare data for the development of big data applications. Big data applications in healthcare are concerned with the analysis of datasets which are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools. The driver for the development of such systems is the continuing effort in making healthcare services more efficient and sustainable. In this paper, we provide a review of current big data applications which utilize physiological waveforms or derived measurements in order to provide medical decision support, often in real time, in the clinical and home environment. We focus mainly on systems developed for continuous patient monitoring in critical care and discuss the challenges that need to be overcome such that these systems can be incorporated into clinical practice. Once these challenges are overcome, big data systems have the potential to transform healthcare management in the hospital of the future.

A flexible active dual-parameter sensor for sensitive temperature and physiological signal monitoring via integrating thermoelectric and piezoelectric conversion

A flexible active dual-parameter sensor for sensitive temperature and physiological signal monitoring has been developed via integrating thermoelectric and piezoelectric conversion.

A flexible, adhesive and self-healable hydrogel-based wearable strain sensor for human motion and physiological signal monitoring.

The advent of hydrogel-based strain sensors has attracted immense research interest in artificial intelligence, wearable devices, and health-monitoring systems. However, the integration of the synergistic characteristics of good mechanical properties, self-adhesiveness, self-healing capability and high strain sensitivity for fabricating hydrogel-based strain sensors is still a challenge. Here, a multifunctional conductive hydrogel composed of a polyacrylamide (PAAm)/chitosan (CS) hybrid network is fabricated for wearable strain sensors. The PAAm network is cross-linked by hydrophobic associations, and the CS network is ionically cross-linked by carboxyl-functionalized multi-walled carbon nanotubes (c-MWCNTs). These two networks are further interlocked by physical entanglement and hydrogen bond interactions. The obtained hydrogels exhibit excellent flexibility, puncture resistance and self-healing capability because of the efficient energy dissipation of the dynamic cross-linking network. Moreover, the hydrogels exhibit self-adhesive behavior on various materials, including polytetrafluoroethylene, wood, glass, aluminum, rubber and skin. Notably, the hydrogels can be applied as soft human-motion sensors for real-time and accurate detection of both large-scale and small human activities, including joint motions, speaking, breathing, and even subtle blood pulsation. Therefore, it is anticipated that the flexible, self-adhesive, self-healing and conductive hydrogel-based strain sensor will have promising applications in artificial intelligence, soft robots, biomimetic prostheses, and personal health care.

A 232–1996-kS/s Robust Compressive Sensing Reconstruction Engine for Real-Time Physiological Signals Monitoring

Compressive sensing (CS) techniques enable new reduced-complexity designs for sensor nodes and help reduce overall transmission power in wireless sensor network [1-2]. Prior CS reconstruction chip designs have been described in [3-4]. However, for real-time monitoring of physiological signals, the applied orthogonal matching pursuit (OMP) algorithms they incorporate are sensitive to measurement noise interference and suffer from a slow convergence rate. This paper presents a new CS reconstruction engine fabricated in 40nm CMOS with following features: 1) A sparsity-estimation framework to suppress measurement noise interference at sensing nodes, achieving at least 8dB signal-to-noise ratio (SNR) gain under the same success rate for robust reconstruction. 2) A new flexible indices-updating VLSI architecture, inspired by the gradient descent method [5], that can support arbitrary signal dimension, (L new , M), of CS reconstruction with high sparsity level (K max ). 3) Parallel-searching, indices-bypassing, and functional blocks that automatically group processing elements (PEs) are designed to reduce the total CS reconstruction cycle latency by 84%. Compared with prior state-of-the-art designs, this CS reconstruction engine can achieve 10x higher throughput rate and 4.2x better energy efficiency at the minimum-energy point (MEP).

Wearable Wireless Body Area Nodes for Remote Physiological Signal Monitoring System

Wearable remote health monitoring systems have gained significant prominence in the recent years due to their growth in technological advances. One form of the Wearable Physiological Monitoring System (WPMS) is the Wearable Body Area Networks (WBAN) used to monitor the health status of the wearer for long durations. The paper discusses a prototype WBAN based wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG) and Electroencephalogram (EEG) acquired using a textile electrode, Photoplethysmogram (PPG), Galvanic Skin Response (GSR), Blood Pressure derived from analysis of Pulse Transmit Time (PTT) and body temperature. The WBAN consists of three sensor nodes that are placed strategically to acquire the physiological signals and the sensor nodes communicate to a chest/wrist worn sink node also known as wearable data acquisition hardware. The sink node receives physiological data from the sensor nodes and is transmitted to a remote monitoring station. The remote monitoring station receives the raw data and it is processed to remove noises, such as power line interference, baseline wander and tremor in the signals and the information is extracted and displayed. The WBANs are designed using the ZigBee wireless communication modules to transmit and receive the data. At the remote monitoring station the physiological parameters such as heart rate, pulse rate, systolic, diastolic blood pressure, GSR and body temperature are continuously monitored from the wearer. The data acquired from the wearable monitoring system is statically validated using a qualified medical device on 34 subjects.

Fabrication of highly pressure-sensitive, hydrophobic, and flexible 3D carbon nanofiber networks by electrospinning for human physiological signal monitoring.

Three-dimensional (3D) porous nanostructure materials have promising applications in pressure sensors or other situations. However, the low sensing sensitivity of these materials restricts precise detection of physiological signals, and it is still a challenge to manufacture highly pressure-sensitive materials, which simultaneously possess other versatile properties. Herein, a simple and cost-efficient strategy is proposed to fabricate versatile 3D carbon nanofiber networks (CNFNs) with superior pressure-sensitivity through electrospinning and thermal treatment. The pressure sensitivity of the CNFNs is 1.41 kPa-1, which is much higher than that of similar 3D porous materials. Unlike traditional carbonaceous materials, the CNFNs exhibit excellent flexibility, stable resilience, and super compressibility (>95%), because of the nano-reinforce of Al2O3. Benefiting from the robust mechanical and piezoresistive properties of the CNFNs, a pressure sensor designed with the CNFNs is able to monitor human physiological signals, such as phonation, pulse, respiration and human activities. An arch-array platform for direction identification of tangential forces and an artificial electronic skin bioinspired by human's hairy skin have been ingeniously designed. The CNFNs also present other versatile characteristics as well, including ultralight density, hydrophobicity, low thermal conductivity, and low infrared emissivity. Therefore, the CNFNs have promising potential in a wide range of applications.

Research of Real Time and Wearable Blood Pressure Monitoring System

In this paper, a wearable medical device for blood pressure monitoring is presented. Its basis is a validated and certified existent medical device. This device was evolved in order to allow new physiological signal monitoring and real time assessment of blood pressure levels by adding a photoplethismography (PPG) probe and the incorporation of new algorithms in an online interface. The device presents accuracies of 94.6% for systolic blood pressure and 92.3% for systolic blood pressure when compared with reference values obtained from a commercial electronic sphygmomanometer.