Vital Physiological Signals Research Articles

Wearable ultrasound bioelectronics for healthcare monitoring

Wearable ultrasound bioelectronics for healthcare monitoring

A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring

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 (reusable/washable), users' comfort ability, integrability, and automatability. In this article we review the characteristics and the performance of the EMG textile electrodes, in the context of functional textile materials, smart textile materials, and smart textile systems for biosignals monitoring. The functional textile materials are confined to signal transmission alone, whereas, the smart textile materials include signal transducers and sensors. The more advanced smart textile systems include signal conditioning circuits with displays. Nowadays, textile-based sensors embedded in garments are becoming a part of users' normal life, in particular, the textile systems that continuously monitor the vital physiological signals from muscles are being sought after in healthcare settings.

Multiple H-bonding chain extender-based polyurethane: Ultrastiffness, hot-melt adhesion, and 3D printing finger orthosis

Mallet finger is a common athletic injury, and the current treatment involves fixing the injured finger by a splint or orthosis. 3D printing technology offers an innovative way to customize patient-specific finger orthosis that fits the individual anthropometrics. However, the existing printed materials cannot combine the high stiffness and toughness to construct an ideal finger orthosis. Herein, we report a new chain extender carrying one amide and one urea group in the side chain, and utilize it to extend polyurethane prepolymer to fabricate a highly stiff and tough thermoplastic polyurethane (TPU). Strong and mobile H-bonds in the side chain promote the formation of multiphase-separated structure in the TPU network, thus endowing this TPU with high Young’s modulus (213.0 ± 3.7 MPa) and high toughness (47.8 ± 1.9 MJ m−3). Due to the dynamic H-bonds in the polymer network, this TPU exhibits an excellent 3D printing ability, and can be used to print finger orthosis by fused deposition modeling technology at an elevated temperature. The printed finger orthosis can be attached with a wireless pulse oximeter by virtue of TPU adhesiveness, thus monitoring vital physiological signals on the injured index finger that can be well fixed by the stiff orthosis.

Lead-free PDMS/PPy based low-cost wearable piezoelectric nanogenerator for self-powered pulse pressure sensor application

Self-powered biocompatible electronic devices to detect vital physiological signals of the human body are in great demand in wearable applications. In this work, a low-cost polydimethylsiloxane/polypyrrole composite polymer film-based piezoelectric nanogenerator is fabricated and utilized as a self-powered arterial pulse pressure sensor. XRD and Raman analysis are used to confirm the crystal structural orientation and chemical composition of composite polymer, respectively. Uniform and crack-free morphology of PDMS/PPy composite polymer film is confirmed via Field Emission Scanning Electron Microscopy, while Energy Dispersive X-Ray is used to study the elemental analysis of composite polymer film, which confirms that there is no extra impurity present in the system. A flexible and robust piezoelectric nanogenerator with device configuration of Al coated PET/(PDMS: PPy)/ ITO coated PET is fabricated that gives an output voltage of 12 V and current density of 0.11 µA/cm2 when 1.47 N/cm2pressure is applied. The biocompatible nanogenerator as a self-powered pulse sensor is demonstrated, thus proving its potential for developing versatile self-powered systems useful in biomedical applications.

Fabrication and implementation of carbon nanotubes for piezoresistive-sensing applications: A review

This paper presents a substantial review of the preparation and implementation of carbon nanotubes (CNTs) for piezoresistive strain-sensing applications. CNT has been a breakthrough material among flexible electronics, and over the last two decades, it has been thoroughly studied and evolved in terms of its fabrication and utilization techniques. The enhanced electromechanical characteristics of CNT allow researchers to produce high-quality sensors for various applications. One of its critical applications is piezoresistive sensors. Indeed, the responses of thin film CNT-based sensors towards applied micro- and macroscale pressures have provided crucial information to the involved application. The significance of piezoresistive sensing is constantly rising in this era of flexible microelectronics. For example, vital physiological signals can be detected through small-scaled structural changes of the sensing prototypes. Sensors like these have been fabricated with a range of process materials and can be deployed for biomedical applications. A review of the exploitation of CNT-based sensors for piezoresistive activities is showcased here. Some common techniques to fabricate CNTs are also mentioned, in addition to the classification of piezoresistive sensing prototypes based on single-walled and multi-walled CNTs.

Noncontact Multiphysiological Signals Estimation via Visible and Infrared Facial Features Fusion

Heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) are vital physiological signals that can reveal the physiological and psychological states of human beings. Recent research studies have demonstrated that these physiological signals can be estimated in a non-contact way from visible or infrared facial videos based on remote Photoplethysmography (rPPG). However, most existing methods only use only one data modality and output a single type of physiological signal at a time. To overcome these restrictions, we propose a multi-task framework named the SMP-Net based on multimodal fusion to realize the non-contact multi-physiological signals estimation. First, a joint attention feature fusion (JAFF) module is designed to encode and fuse features from visible and infrared videos. The JAFF module considers different modality-wise, spatial, and channel-wise information on features comprehensively. Then, a task-oriented feature refinement (TOFR) module is developed to extract task-oriented features by refining shared features to improve the estimation performance. Finally, the proposed SMP-Net is validated on the MMVS, VIPL-HR, and UBFC-rPPG datasets and outperforms state-of-the-art methods. Our proposed SMP-Net achieves 1.12 bpm mean absolute error (MAE) for HR estimation, 0.58 ρ for rPPG estimation, and 2.08 MAE for RR estimation on the MMVS dataset. On the VIPL-HR and UBFC-rPPG datasets, the MAE of HR estimation is 2.03 and 0.59 bpm, respectively. The proposed SMP-Net is of great significance for continuous non-contact estimation of multi-physiological signals.

Liquid Metal Enabled Biodevices

Biodevices are crucial for monitoring vital physiological signals, managing chronic health conditions, developing artificial organs for assisting people with disabilities, and conducting various clinical and surgical procedures. However, existing biodevices are mostly composed of rigid components, which can cause discomfort to the user, whereas the high stiffness of implants is known to be the major cause of inflammation and scarring. Gallium‐based liquid metals are intrinsically soft and possess desirable properties, including low toxicity, high conductivity, and deformability, which make them ideally suited for developing soft, deformable, reconfigurable, and healable biodevices. Herein, recent advancements in the emerging field of liquid‐metal‐based biodevices are discussed. This includes a description of the properties of gallium‐based liquid metals which make them so distinct from conventional materials, a brief outline of various techniques devised for fabrication of liquid‐metal‐based devices/structures, and an overview of the diverse range of wearable or implantable liquid‐metal‐enabled biodevices. The outlook and challenges are also discussed.

Evolving Flexible Sensors, Wearable and Implantable Technologies Towards BodyNET for Advanced Healthcare and Reinforced Life Quality

Recent advances in flexible electronics have offered a good opportunity in designing a variety of wearable devices for healthcare monitoring, prevention medicine, and robotic control. Wearable sensors are entering the era of digital health, with compelling functionalities in recording vital signs, physiological signals, body kinetics, and dynamic biomolecular state. Meanwhile, energy harvesters that scavenge waste energy from body motions and the ambient environment have received intense attention, which are expected to realize self-powered or energy-autonomous systems. Moving towards the inside of the body, the implantable device also plays an indispensable role in monitoring key biomedical and physiological information and effective treatment of chronic diseases. Besides, wearable robotic exoskeletons have enabled advanced functionalities of assisting or augmenting human mobilities unprecedently. These technologies and platforms will be merged, giving way to the bodyNET: a network of wearable sensors, implantable devices, and exoskeletons for improved healthcare and health outcomes. In this article, we offer a brief review of recent advances in flexible and wearable sensors and summarize the progress of self-powered wearable sensors based on piezoelectric and triboelectric nanogenerators. We also discuss the implantable devices and self-powered neuromodulation systems and offer recent works of lower-limb exoskeletons. Lastly, we present a future trend towards a comprehensive and capable bodyNET for advanced healthcare and reinforced life quality.

Modulation of Efficient Diiodo-BODIPY in vitro Phototoxicity to Cancer Cells by Carbon Nano-Onions.

Photodynamic therapy is currently one of the most promising approaches for targeted cancer treatment. It is based on responses of vital physiological signals, namely, reactive oxygen species (ROS), which are associated with diseased condition development, such as tumors. This study presents the synthesis, incorporation, and application of a diiodo-BODIPY–based photosensitizer, based on a non-covalent functionalization of carbon nano-onions (CNOs). In vitro assays demonstrate that HeLa cells internalize the diiodo-BODIPY molecules and their CNO nanohybrids. Upon cell internalization and light exposure, the pyrene–diiodo-BODIPY molecules induce an increase of the ROS level of HeLa cells, resulting in remarkable photomediated cytotoxicity and apoptosis. Conversely, when HeLa cells internalize the diiodo-BODIPY/CNO nanohybrids, no significant cytotoxicity or ROS basal level increase can be detected. These results define a first step toward the understanding of carbon nanomaterials that function as molecular shuttles for photodynamic therapeutics, boosting the modulation of the photosensitizer.

Holistically Engineered Polymer-Polymer and Polymer-Ion Interactions in Biocompatible Polyvinyl Alcohol Blends for High-Performance Triboelectric Devices in Self-Powered Wearable Cardiovascular Monitorings.

The capability of sensor systems to efficiently scavenge their operational power from stray, weak environmental energies through sustainable pathways could enable viable schemes for self-powered health diagnostics and therapeutics. Triboelectric nanogenerators (TENG) can effectively transform the otherwise wasted environmental, mechanical energy into electrical power. Recent advances in TENGs have resulted in a significant boost in output performance. However, obstacles hindering the development of efficient triboelectric devices based on biocompatible materials continue to prevail. Being one of the most widely used polymers for biomedical applications, polyvinyl alcohol (PVA) presents exciting opportunities for biocompatible, wearable TENGs. Here, the holistic engineering and systematic characterization of the impact of molecular and ionic fillers on PVA blends' triboelectric performance is presented for the first time. Triboelectric devices built with optimized PVA-gelatin composite films exhibit stable and robust triboelectricity outputs. Such wearable devices can detect the imperceptible skin deformation induced by the human pulse and capture the cardiovascular information encoded in the pulse signals with high fidelity. The gained fundamental understanding and demonstrated capabilities enable the rational design and holistic engineering of novel materials for more capable biocompatible triboelectric devices that can continuously monitor vital physiological signals for self-powered health diagnostics and therapeutics.

An alternative method of biomedical signal transmission through the GSM voice channel

In this work, a new solution for online and accurate biomedical data transmission is presented. For this purpose, a global system for mobile (GSM) communication voice channel is, for the first time, used as a communication link between the patient and healthcare provider. Biomedical signals are converted into speech-like signals before being transferred over a GSM voice channel. On the receiver side, speech-like symbols are stored in a symbols bank, and constructed using random stochastic signals. On the receiver end, the index of the symbol with the most similarity to the received signal is selected as the identified sample. This method enables us to communicate with an accuracy of 99.8% at a transfer rate of 110 samples per second and signal-to-noise ratio (SNR) of 10. By utilizing a GSM voice channel, any voice channel, such as a cell phone, can be used for data transmission. The transmitted signal is encoded; therefore, the connection is secured. GSM technology has benefits such as availability, reliability, and robustness. Additionally, GSM can be used as a backup or service for transmitting vital physiological signals in emergency situations (e.g. in an ambulance). This technology can also be used to transmit other physiological signals as well as nonphysiological generic data.

Hydrogen Sulfide Switch Phenomenon Regulating Autophagy in Cardiovascular Diseases.

Hydrogen sulfide (H2S), a novel gaseous signaling molecule, is a vital physiological signal in mammals. H2S protects the cardiovascular system via modulation of vasodilation, vascular remodeling, and inhibition of vascular calcification, and also has anti-atherosclerosis properties. Autophagy is a lysosomal-mediated intracellular degradation mechanism for excessive or abnormal proteins and lipids. The contribution of autophagy to normal and disease-state cell physiology is extremely complicated. Autophagy acts as a double-edged sword in the cardiovascular system. It can defend against damage to cells caused by environmental changes and it can also induce active cell death under certain conditions. In recent years, accumulating evidence indicates that H2S can up- or downregulate autophagy in many pathological processes, thereby switching from a harmful to a beneficial role. In this review, we summarize progress on understanding the mechanism by which H2S regulates autophagy in cardiovascular disease. We also discuss a H2S switch phenomenon that regulates autophagy and provides protection in cardiovascular diseases.

A noninvasive method for monitoring respiratory rate of rats based on a microcantilever resonant humidity sensor

Respiratory rate is a vital physiological signal in laboratory rodents research. This paper presents a noninvasive method based on resonant humidity sensor for monitoring respiratory rate of rats. The sensor is composed of an aluminum nitride (AlN) resonant microcantilever coated with graphene oxide (GO) film. The sensor shows high sensitivity and good repeatability. By combining the sensor with a control circuit, a closed-loop respiration measurement system is realized for fast response. The system can monitor respiratory rate of rats in real time by detecting relative humidity difference between exhaled and inhaled breath. The method has advantages of fast response, small size and low cost. Furthermore, the respiratory rate of rats can be noninvasively monitored by integrating the sensor with a face mask.

Constrained error rate analysis for wireless body area networks

Wireless body area network (WBAN) is composed of miniaturised sensors that operate in the vicinity of the human body for recording the vital physiological signals and wirelessly transmitting them to a central hub for further processing. In this study, a statistical approach is applied to an experimental channel data set to extract the models for the squared channel gain that best describe the characteristics of the transmission medium between the sensors and the central hub. The derived models are then utilised to investigate the error rate performance of WBAN sensors. On the basis thereof, an optimisation problem is formed for which the cost function is the symbol error rate (SER) metric. The constraints to be appended to the problem reflect the fact that the communication among the nodes is envisaged to be long-lasting and harmless to the human tissues. Depending on the way that the constraints are combined, four possible problems are defined. Subsequently, the method of Lagrange multipliers is deployed to find the closed-form expression for the optimal energy allocation that results in error rate minimisation. The numerical results cast light on the capabilities of the devised analytical framework and the insight that it provides into a WBAN operation.

WearBreathing

Respiratory rate is a vital physiological signal that may be useful for a multitude of clinical applications, especially if measured in the wild rather than controlled settings. In-the-wild respiratory rate monitoring is currently done using dedicated chest band sensors, but these devices are specialized, expensive and cumbersome to wear day after day. While recent works have proposed using smartwatch based accelerometer and gyroscope data for respiratory rate monitoring, current methods are unreliable and inaccurate in the presence of motion and have therefore only been applied in controlled or low-motion settings. Thus, measuring respiratory rate in the wild remains a challenge. We observe that for many applications, having fewer accurate readings is better than having more, less accurate readings. Based on this, we develop WearBreathing, a novel system for respiratory rate monitoring. WearBreathing consists of a machine learning based filter that detects and rejects sensor data that are not suitable for respiratory rate extraction and a convolutional neural network model for extracting respiratory rate from accelerometer and gyroscope data. Using a diverse, out-of-the-lab dataset that we collected, we show that WearBreathing has a 2.5 to 5.8 times lower mean absolute error (MAE) than existing approaches. We show that WearBreathing is tunable and by changing a single threshold value, it can, for example, deliver a reading every 50 seconds with a MAE of 2.05 breaths/min or a reading every 5 minutes with an MAE of 1.09 breaths/min. Finally, we evaluate power consumption and find that with some power saving measures, WearBreathing can run on a smartwatch while providing a full day's worth of battery life.

Analysis of a Contactless ECG Monitoring System

Electrocardiography is one of the vital physiological signals which gives information about the different phases of cardiac activity. Monitoring the activity of the heart plays a major role in timely treatment and thereby reduces the number of deaths due to cardiac diseases. As there are several problems posed by the conventional ECG monitoring system for long term applications, a contactless ECG monitoring system can be used as an alternative. But there are several downsides to contactless ECG monitoring system as well, which prevent it from reaching the standards required for clinical use. Hence, an analysis of the different factors that can possibly affect the quality of the ECG signal being acquired using the contactless system is required, in order to understand the problems better and to overcome them. The different factors that are considered in the present analysis are change in dielectric material, change in thickness of the dielectric, effect of motion artefacts, and effect of stray and cable capacitances.

Paper/Carbon Nanotube-Based Wearable Pressure Sensor for Physiological Signal Acquisition and Soft Robotic Skin.

A wearable and flexible pressure sensor is essential to the realization of personalized medicine through continuously monitoring an individual's state of health and also the development of a highly intelligent robot. A flexible, wearable pressure sensor is fabricated based on novel single-wall carbon nanotube /tissue paper through a low-cost and scalable approach. The flexible, wearable sensor showed superior performance with concurrence of several merits, including high sensitivity for a broad pressure range and an ultralow energy consumption level of 10-6 W. Benefited from the excellent performance and the ultraconformal contact of the sensor with an uneven surface, vital human physiological signals (such as radial arterial pulse and muscle activity at various positions) can be monitored in real time and in situ. In addition, the pressure sensors could also be integrated onto robots as the artificial skin that could sense the force/pressure and also the distribution of force/pressure on the artificial skin.

Noncontact monitoring breathing pattern, exhalation flow rate and pulse transit time.

We present optical imaging-based methods to measure vital physiological signals, including breathing frequency (BF), exhalation flow rate, heart rate (HR), and pulse transit time (PTT). The breathing pattern tracking was based on the detection of body movement associated with breathing using a differential signal processing approach. A motion-tracking algorithm was implemented to correct random body movements that were unrelated to breathing. The heartbeat pattern was obtained from the color change in selected region of interest (ROI) near the subject's mouth, and the PTT was determined by analyzing pulse patterns at different body parts of the subject. The measured BF, exhaled volume flow rate and HR are consistent with those measured simultaneously with reference technologies (r = 0.98, for HR; r = 0.93, for breathing rate), and the measured PTT difference (30-40 ms between mouth and palm) is comparable to the results obtained with other techniques in the literature. The imaging-based methods are suitable for tracking vital physiological parameters under free-living condition and this is the first demonstration of using noncontact method to obtain PTT difference and exhalation flow rate.

Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models

Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. We have developed new techniques of signal reconstructions based on iterative retraining and accumulated averaging of neural networks. The effectiveness and robustness of these techniques are demonstrated using data records from the Computing in Cardiology/PhysioNet Challenge 2010. The average correlation coefficient between prediction and target for 100 records of various target signals is about 0.9. We have also explored influences of a few important parameters on the accuracy of reconstructions. The developed techniques may be used to detect changes in patient state and to recognize intervals of signal corruption.

Overview of MEMSWear II - Incorporating MEMS Technology into smart shirt for Geriatric care

This paper presents a method of using smart sensors for continuous detection of vital physiological and physical gait signals that can be relayed to a fall prediction algorithm for predicting an imminent faint fall. A novel MEMS based BP sensor will be discussed briefly. Once an imminent faint fall is detected, fall prevention and injury minimization devices will be activated. A body area network consisting sensor circuits utilizing short range ISM communication will be used to enable signal transmission to a processor which acts as a communication gateway as well. This processor would then encode and send the signals via Bluetooth to designated devices to effect inform caregivers or family members.