Physiological Signal Monitoring Research Articles

Carbon Nanotube Network Topology-Enhanced Iontronic Capacitive Pressure Sensor with High Linearity and Ultrahigh Sensitivity.

Flexible capacitive pressure sensors with high sensitivity over a wide pressure range are highly anticipated in the fields of tactile perception and physiological signal monitoring. However, despite the introduction of microstructures on the electrolyte layer, the deformability is still limited due to the size limitation of the microstructures, making it difficult to significantly improve the sensitivity of iontronic capacitive pressure sensors (ICPSs). Here, we propose an innovative strategy of combining carbon nanotubes (CNTs) topological networks and ionic hydrogel micropyramid array microstructures that can significantly enhance the sensitivity of flexible ICPSs for ultrasensitive pressure detection. Compared with other previously reported ICPSs, the sensor developed in this work exhibits an unprecedented sensitivity (Smin > 1050 kPa-1) and a high linear response (R2 > 0.99) in a wide pressure range (0.03-28 kPa) enabled by CNT percolation networks inside the microstructred electrolyte layer. This ultrasensitive and flexible ICPS also can effectively detect pressure from a variety of sources, including sound waves, lightweight objects, and tiny physiological signals, such as pulse rate and heartbeat. This work provides a general strategy to achieve an ICPS with both broader pressure-response range and higher sensitivity, which provides a stable and efficient way for a low-cost, scalable sensor for sensitive tactile sensing in human-computer interaction applications.

ATHENA I—An architecture for near real-time physiological signal monitoring and pattern detection

Smart healthcare has wide application possibilities, including physiological signals analysis. With the development and improvement of microelectronic devices and sensors, wearable sensors can acquire physiological signals with quality and support remote health monitoring. Due to the growing use of these devices, there is a need for easy and flexible ways to use them in health monitoring. To contribute in this context, we developed the ATHENA I, an architecture focused on using wearables to acquire physiological signals and applying artificial intelligence to classify health patterns in real-time. Because of the constant improvement of wearable devices, we focused on developing a flexible and efficient architecture. Since many health centers outside the central regions of the metropolis have reduced connectivity, we designed an architecture capable of operating entirely outside the data network. The architecture prototype features BITalino wearable sensor and a Raspberry Pi single-board computer. For a complete prototype evaluation, we performed stress-inducing experiments conducted by psychologists to acquire a new dataset of physiological signs. We used this dataset in pattern classification in offline operations and near real-time situations. Our purpose with these experiments is to predict stress, being able to differentiate the normal state from the stress state. The classification results for the stress patterns showed promising results, with accuracy above 98.72% for binary classification (stress, no-stress); and 92.72% for classification with three classes (pre-stress, stress, and post-stress). In the real-time classification, we obtained an accuracy of 69.00%. The architecture presented excellent communication and operation stability. During the experiments, the architecture performed short and long acquisitions efficiently.

A vital signsignal noise suppression method for wearable piezoelectric devices.

This paper tackles the problem of noise suppression during vital signsignal monitoring. Physiological signal monitoring is a significant and promising medical monitoring method, and wearable medical monitoring devices based on piezoelectric polymer sensors are a trending way for their advantages of being flexible in the shape, portable to use, and comfortable to wear. However, this raises the question that the measured signal contains much more noise components. To avoid the following shortcoming of low signal to noise ratio (SNR), a noise suppression method based on improved wavelet threshold and empirical mode decomposition combined with singular value decomposition (SVD) screening the intrinsic mode function (IMF) components is proposed. A wavelet transform is first used under the combination of hard and soft thresholds to focus the target range in the low-frequency region where the energy of the physiological signal is concentrated. Then, a complete ensemble empirical mode decomposition is used to decompose the signal effectively, which can resist the influence of random noises. Meanwhile, a SVD decomposition procedure was used to filter out the lower correlated IMF components to retain the validity of the original signal. We verified the effectiveness of the proposed method through simulated and measured experiments as well as the advantages and disadvantages of the algorithm compared with other physiological signal denoising algorithms through SNR filtering results, power spectrum distribution, and other perspectives. The results proved that the proposed method could effectively remove more detailed noise and improve the SNR of the signal efficiently, which is more conducive to the demand for auxiliary medical diagnosis in the future.

Self-Healable PEDOT:PSS-PVA Nanocomposite Hydrogel Strain Sensor for Human Motion Monitoring.

Strain sensors based on conducting polymer hydrogels are considered highly promising candidates for wearable electronic devices. However, existing conducting polymer hydrogels are susceptible to aging, damage, and failure, which can greatly deteriorate the sensing performance of strain sensors based on these substances and the accuracy of data collection under large deformation. Developing conductive polymer hydrogels with concurrent high sensing performance and self-healing capability is a critical yet challenging task to improve the stability and lifetime of strain sensors. Herein, we design a self-healable conducting polymer hydrogel by compositing poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers and poly(vinyl alcohol) (PVA) via both physical and chemical crosslinking. This PEDOT:PSS-PVA nanocomposite hydrogel strain sensor displays an excellent strain monitoring range (>200%), low hysteresis (<1.6%), a high gauge factor (GF = 3.18), and outstanding self-healing efficiency (>83.5%). Electronic skins based on such hydrogel strain sensors can perform the accurate monitoring of various physiological signals, including swallowing, finger bending, and knee bending. This work presents a novel conducting polymer hydrogel strain sensor demonstrating both high sensing performance and self-healability, which can satisfy broad application scenarios, such as wearable electronics, health monitoring, etc.

Dual-Layer All-Textile Flexible Pressure Sensor Coupled by Silver Nanowires with Ti3C2-Mxene for Monitoring Athletic Motion during Sports and Transmitting Information.

At present, wearable flexible pressure sensors have broad application prospects in fields such as motion monitoring and information transmission. However, it is still a challenge to design flexible pressure sensors with high sensitivity over a large sensing range and simple fabrication. Here, we use a simple "dipping-drying" method to fabricate a fabric-based flexible pressure sensor by coupling silver nanowires (AgNWs) with Ti3C2-MXene. The interaction between MXene and AgNWs helps realize a dual-layer sensing network, achieving good synergistic effects between pressure sensitivity and sensing range. The effects of the material combination and dip-coating sequence on the sensor's performance are systematically studied. The results show that the sensor was impregnated sequentially with AgNWs solution, and the MXene solution has the highest sensitivity (0.168 kPa-1) over a wide range (190 kPa). Meanwhile, it has the advantages of low response hysteresis and detection limit, as well as good linearity and durability. We further demonstrate the application of this sensor in human physiological signal monitoring and motion pattern recognition. It can also encrypt and transmit information according to different pressing states. In addition, the proposed pressure sensor array exhibits spatial resolution detection capabilities, laying the foundation for applications in the fields of motion monitoring and human-computer interaction.

(Invited) smart Wearable Electronics for Chronic Disease Management

60% of Americans live with at least one chronic disease. These diseases and associated comorbidities are now the leading causes of death in the United States. Effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable devices to precisely determine the pathological state. These wearable physiological signal monitoring can dramatically reduce the demand of physician visiting and increase patients’ engagement and treatment adherence rate. Although wearable bioelectronic devices have shown huge potential in chronic disease management, existing technology still mostly relies on rigid chip components, where the interface between chips and skin/tissue has been a bottleneck that limits the system performance (noise, robustness etc). To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart wearable electronics that targets next-generation chronic disease management.Here I would like to discuss two of my developed technology platforms to elaborate the concept of smart wearables. First, I will describe a body area sensor network technology platform. In the system design concept side, this technology utilizes a novel wireless hybridization strategy and an unconventional detuned RFID working regime that provide an ideal skin interface. In the material/device side, this system integrates several skin-conforming polymer-based soft devices (ring oscillators, RF diodes, antennas, transistors etc.) as an integrated soft multimode sensing sticker. (1,2) Next, I will describe a wireless closed-loop smart bandage that can accelerate and monitor the wound healing process. (3) Such smart bandage also integrates new design concepts (wireless, battery-free design that combines both sensing and stimulation), novel materials (low-impedance, high-toughness, and tunable adhesion hydrogel as skin interface) and closed-loop control algorithms. Overall, the developed technology platform could assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help to reduce chronic disease burden, lower medical costs, and provide a better quality of life for patients.

In-situ grown a zeolitic imidazolate framework for boosting sensitivity of breathable wearable electronics

Wearable electronics have emerged as versatile platforms for various biomedical applications. However, developing sensors that are both air-permeable and highly sensitive for long-term, subtle physiological signal monitoring remains a challenge. To overcome this, an innovative in-situ growth method was employed to decorate electrospun polyvinylidene fluoride (PVDF) nanofibers with a zeolitic imidazolate framework (known as ZIF-8). This approach has led to a remarkable 300% increase in sensitivity (5.94 kPa−1) compared to sensors prepared using pure PVDF nanofiber (1.42 kPa−1). This in-situ growth method effectively prevented ZIF-8 agglomeration, contributing to the desired improvement in permittivity (∼1.6), unlike blended ZIF-8/PVDF nanofiber. Furthermore, atomic force microscope and simulations were utilized to demonstrated that the nano-scale structures formed by the uniform growth of ZIF-8 significantly contribute to the enhanced sensitivity of the sensor. In addition to satisfactory air-permeability (10 mm/s) and high sensitivity, the as-prepared sensor exhibits excellent flexibility, enabling it to conform to irregular organ surfaces for real-time monitoring of pulse, respiration, and swallowing. This approach holds great promise for the development of highly sensitive and skin-friendly wearable electronics, offering significant benefits for healthcare advancement.

Design of a Biaxially Stretchable Microstrip Antenna Enabled by Buckled Au Thin Film on an Elastomeric Substrate

Wearable wireless biomedical electronics enable monitoring and wireless transmission of patient physiological and pathological signals to provide remote guidance for appropriate diagnosis and treatment. As a core component, the antenna must be flexible and stretchable to adapt to the complex mechanical deformations (e.g., stretching, bending, and twisting) induced by human motions. This work proposes a biaxially stretchable microstrip antenna based on buckled gold thin films bonded on an elastomeric substrate. A simplified analytic model validated by simulations and experiments is established to investigate the biaxial buckling behaviors of the thin films within 10% tensile strains. The properties, including resonance frequency, bandwidth, and radiation pattern of the fabricated biaxially stretchable microstrip antenna under various stretched states, are studied by combining experiments and finite element analysis. The effects of biaxial tensile deformations on the resonance frequency, bandwidth, and radiation properties are discussed. Results show that the designed microstrip antenna has a relatively stable performance under both natural and deformed states within 10% of uniaxial and biaxial tensile strains, which enables the designed antenna to have broad application prospects in wearable wireless medical devices for stable transmission of signals between body-worn sensors and terminals, especially for situations accompanied with complex deformations.

A fingertip-type magnetic pulse detection device for unusual monitoring conditions

Wearable devices for continuous non-invasive monitoring of physiological signals are crucial for preventive care and management of chronic conditions. However, these devices are either sensitive to skin conditions or require external stimuli, such as light or electrical excitation, to penetrate the skin for pulse detection. This often results in large motion artifacts and unsuitability for certain skin conditions. Here we demonstrate a simple fingertip-type device which can detect clear pulse signals under a range of conditions, including fingers covered by opaque substances such as a plaster or nail polish, or fingers immersed in water. The structure of the device consists of a pair of magnets and a magnetic sensor. Both experimental and numerical results show that the detected pulsation signals correspond directly to the vibrations induced by blood circulation with negligible influence from modulated magnetic signature of blood. Therefore, this device could be used in conditions which are challenging for existing pulse detectors and might also have its application extended to blood pressure measurement.

High-Linearity Flexible Pressure Sensor Based on the Gaussian-Curve-Shaped Microstructure for Human Physiological Signal Monitoring.

Flexible pressure sensors with high-performance show broad application prospects in health monitoring, wearable electronic devices, intelligent robot sensing, and other fields. Although flexible pressure sensors have made significant progress in sensitivity and detection range, most of them still exhibit strong nonlinearity, which leads to significant troubles in signal acquisition and thus limits their popularity in practical applications. It remains a serious challenge for the flexible pressure sensor to achieve high linearity while maintaining high sensitivity. Herein, a doped sensing membrane with a uniformly distributed Gaussian-curve-shaped micropattern array was developed using the micro-electromechanical systems (MEMS) process, and a flexible sensor structure with the doped film as the core was designed and constructed. The prototype sensor has a high sensitivity of 1.77 kPa-1 and a linearity of 0.99 in the full detection range of 20 Pa to 30 kPa. In addition, its excellent performance also includes fast response/recovery times (∼25/50 ms) and long-term endurance (>10,000 cycles at 15 kPa). The prototype sensor has been successfully demonstrated in human pulse monitoring, speech recognition, and gesture recognition. The 2 × 6 sensor array can detect the spatial pressure distribution. Thus, such a microstructure shape design will open a new way to fabricate a high-linearity pressure sensor for potential applications in health monitoring, human-machine interaction, etc.

MetaPhys: Contactless Physiological Sensing of Multiple Subjects Using RIS-Based 4-D Radar

Contactless physiological signal sensing is an emerging technology for routine health monitoring, ambient-assisted living, automotive, search and rescue, and public security. In this work, we propose MetaPhys, a reconfigurable intelligent surface (RIS)-based contactless physiological signal monitoring system that can simultaneously sense multiple persons and their respiratory and cardiac signals. The proposed system localizes the human targets by utilizing RIS to dynamically manipulate the electromagnetic waves in the environment for beamforming and 3D radar imaging in the spatial dimension. Then it extracts the respiration and cardiac signals from the sequential echoes in the temporal dimension. The high-resolution information in the four dimensions as “3D space + 1D time" makes it comprehensively perceive the targets and environment. The beamforming allows the radiated energy to focus on the thoraxes, which helps reduce interference and distortion caused by stationary reflectors (i.e., clutters, static body parts, and multipath), thereby improving the signal-to-noise ratio and enabling long-range measurements. We also verify the measured data in a real indoor environment, and the experimental results show that the proposed system can accurately monitor the physiological signals of multiple subjects. The root mean square errors of respiratory rate and heart rate at a distance of 3 meters are 0.2 breaths per minute and 1.1 beats per minute, respectively.

Near-IR Embedded in a Physiological Signal Monitoring System

Near-IR Embedded in a Physiological Signal Monitoring System

Stretchable, self-healing, biocompatible, and durable ionogel for continuous wearable strain and physiological signal monitoring

Electronic textiles (e-textiles) have great potential for use in wearable systems worn next to skin for continuous physiological monitoring, owing to their flexibility and conformability to the body. However, challenges exist in the long-term functionality and durability of these materials. This work presents a highly stretchable, self-healing, and biocompatible chloride-based ionogel fabricated from polymerization of thioctic acid, and 1-n-Butyl-3-methylimidazolium chloride ([BMIM]Cl) ionic liquid. With its adaptable mechanical properties and tunable viscosity by simple dilution, the ionogel precursor can be either cast into an ionogel film used for strain sensing applications up to 500% strain, or directly sprayed as a coating for e-textile electrodes for biopotential signal monitoring. The ionogel can reform at room temperature, demonstrating 73.5% stress recovery, and 84.8% modulus recovery after a 30-minute healing period. When used as a spray barrier coating on e-textiles, the ionogel displayed high resistance to abrasion, and reformed after adding ethanol to the surface. The electrical properties of the ionogel coated e-textiles satisfies the performance range desired for textile sensing electrodes. Further development and application of this ionogel material system can address long-term durability concerns by extending the usable lifetime of the sensors for wearable applications.

A tough and strain-stiffening ionogel enabled by moderate microphase separation for epidermal multi-sensor

Stretchable ionogels demonstrate promising potentials in the next-generation epidermal electronics, due to their high conductivity, non-volatility and thermal stability. Nevertheless, the typical plasticization of ionic liquids (ILs) renders it challenging to achieve high toughness with skin-like strain-stiffening feature. Here we introduce moderate microphase separation structure by randomly polymerizing two types of monomers with different compatibility with ILs to obtain an ionogel with two-phase structure (continuous phase and microphase). The homogenous continuous phase endows the ionogel with high stretchability (over 1000% strain) via the reversible breakage of ion-dipole and dipole-dipole interactions, while the moderate microphase can strengthen the ionogel at large deformation. These two phases cooperate to realize a high toughness (5.26 MJ/m3) with strain-stiffening feature (“S” shaped stress-strain curve). Moreover, this ionogel also shows brilliant ant-swelling capability in multiple solvents, self-healing capability and high sensitivity to strain and temperature, inspiring us to explore the application as epidermal multi-sensor for human motions detection, air velocity recognition and physiological signal monitoring in aquatic environments.

Wrinkle Clamp Down on Structure Crack Strain Sensor Based on High Poisson's Ratio Material for Home Health Monitoring and Human-Machine Interaction.

Flexible wearable crack strain sensors are currently receiving significant attention because they can be used in a wide range of physiological signal monitoring and human-machine interaction applications. However, sensors with high sensitivity, great repeatability, and wide sensing range remain challenging. Herein, a tunable wrinkle clamp down structure (WCDS) crack strain sensor based on high Poisson's ratio material with high sensitivity, high stability, and wide strain range is proposed. Based on the high Poisson's ratio of the acrylic acid film, the WCDS was prepared by a prestretching process. The wrinkle structures can clamp down the crack to improve the cyclic stability of the crack strain sensor while maintaining its high sensitivity. Moreover, the tensile properties of the crack strain sensor are improved by introducing wrinkles in the bridge-like gold stripes connecting each separated gold flake. Owing to this structure, the sensitivity of the sensor can reach 3627, stable operation over 10 000 cycles is achieved, and the strain range can reach about 9%. In addition, the sensor exhibits low dynamic response and good frequency characteristics. Because of its demonstrated excellent performance, the strain sensor can be used in pulse wave and heart rate monitoring, as well as posture recognition and game control.

Underwater Self-Healing and Recyclable Ionogel Sensor for Physiological Signal Monitoring.

Ionogels with self-healing properties have become more and more desirable because they can improve the reliability, safety, and fatigue-resistant performance of flexible devices. However, the self-healing property of ionogels is usually susceptible to water molecules, and the application of ionogel sensors is limited to the atmospheric environment. Inspired by gelatinous jellyfish, herein, an underwater self-healing ionogel was prepared via one-step photoinitiated polymerization of acrylic acid 2,2,2-trifluoroethyl ester and N-isopropylacrylamide (NIPAm) in a hydrophobic ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIm][TFSI]). The dynamic physical interactions (hydrogen bonding and ion-dipole interactions) endowed the ionogel with remarkable transparency, underwater self-healing (up to 96%), toughness (3.93 MJ m-3), and underwater adhesion. And the cross-linking ionogel could be green recycled by ethanol for further application. Especially, the ionogel-based sensor presented excellent strain and pressure sensing sensitivity, rapid responsiveness (140 ms), and ultrastability. The ionogel could be further assembled into an optical camouflage sensor to detect and distinguish different human motions in real time with high sensitivity, stability, and repeatability, as well as for underwater electrocardiography monitoring wirelessly. This ionogel provides a promising strategy for the development of underwater self-healing sensors.

Wafer-patterned, permeable, and stretchable liquid metal microelectrodes for implantable bioelectronics with chronic biocompatibility.

Implantable bioelectronics provide unprecedented opportunities for real-time and continuous monitoring of physiological signals of living bodies. Most bioelectronics adopt thin-film substrates such as polyimide and polydimethylsiloxane that exhibit high levels of flexibility and stretchability. However, the low permeability and relatively high modulus of these thin films hamper the long-term biocompatibility. In contrast, devices fabricated on porous substrates show the advantages of high permeability but suffer from low patterning density. Here, we report a wafer-scale patternable strategy for the high-resolution fabrication of supersoft, stretchable, and permeable liquid metal microelectrodes (μLMEs). We demonstrate 2-μm patterning capability, or an ultrahigh density of ~75,500 electrodes/cm2, of μLME arrays on a wafer-size (diameter, 100 mm) elastic fiber mat by photolithography. We implant the μLME array as a neural interface for high spatiotemporal mapping and intervention of electrocorticography signals of living rats. The implanted μLMEs have chronic biocompatibility over a period of eight months.

Wearable ultrasound bioelectronics for healthcare monitoring

Wearable ultrasound bioelectronics for healthcare monitoring

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

Multifunctional Biosensing Platform Based on Nickel-Modified Laser-Induced Graphene.

Nickel plating electrolytes prepared by using a simple salt solution can achieve nickel plating on laser-induced graphene (LIG) electrodes, which greatly enhances the electrical conductivity, electrochemical properties, wear resistance, and corrosion resistance of LIG. This makes the LIG-Ni electrodes well suited for electrophysiological, strain, and electrochemical sensing applications. The investigation of the mechanical properties of the LIG-Ni sensor and the monitoring of pulse, respiration, and swallowing confirmed that the sensor can sense insignificant deformations to relatively large conformal strains of skin. Modulation of the nickel-plating process of LIG-Ni, followed by chemical modification, may allow for the introduction of glucose redox catalyst Ni2Fe(CN)6 with interestingly strong catalytic effects, which gives LIG-Ni impressive glucose-sensing properties. Additionally, the chemical modification of LIG-Ni for pH and Na+ monitoring also confirmed its strong electrochemical monitoring potential, which demonstrates application prospects in the development of multiple electrochemical sensors for sweat parameters. A more uniform LIG-Ni multi-physiological sensor preparation process provides a prerequisite for the construction of an integrated multi-physiological sensor system. The sensor was validated to have continuous monitoring performance, and its preparation process is expected to form a system for non-invasive physiological parameter signal monitoring, thus contributing to motion monitoring, disease prevention, and disease diagnosis.