Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

Intelligent Ureteral Stent Placeable via Standard Procedure for Kidney Pressure Telemetry: An Ex-Vivo Study

This paper reports the first telemetric ureteral stent compatible with common placement procedure, enabling wireless sensing and detection of ureteral obstruction and resultant kidney swelling known as hydronephrosis at an early stage. This sensor-integrated "intelligent" ureteral stent is prototyped via the design and fabrication approaches that raise the practicality of the device and tested in a harvested swine kidney-ureter model ex vivo. Leveraging a polymeric double-J stent and micro-electro-mechanical systems technology, the intelligent stent is built by embedding micro pressure sensors and a radiofrequency antenna, forming a resonant circuit that enables wireless kidney pressure monitoring in an operating frequency of 40-50 MHz. The stent device is entirely packaged with Parylene-C for both biocompatibility and electrical insulation of the device in order to function in the real environment including urine, an electrically conductive liquid. A comparison between the results measured in in-vitro and ex-vivo settings show a good match in the sensitivity to applied pressure. In particular, the ex-vivo test in the kidney-ureter model pressurized with artificial urine in a cycled manner demonstrates wireless pressure tracking with a response of 1.3 kHz/mmHg, over pressures up to 37 mmHg that well covers a range of pressure increase known for chronic obstruction. This testing is enabled by the prototype placement into the ex-vivo model using the standard stenting technique and tools without noticeable functional degradation or failures, showing potential compatibility of the device with today's clinical need as a ureteral stent.

Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors

BACKGROUND

Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF.

OBJECTIVES

In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF.

METHODS

Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method.

RESULTS

A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39% ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation.

CONCLUSIONS

Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI; NCT04444583; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors; NCT04290091).

Flexible Strain Sensors Based on an Interlayer Synergistic Effect of Nanomaterials for Continuous and Noninvasive Blood Pressure Monitoring

The continuous, noninvasive monitoring of human blood pressure (BP) through the accurate detection of pulse waves has extremely stringent requirements on the sensitivity and stability of flexible strain sensors. In this study, a new ultrasensitive flexible strain sensor based on the interlayer synergistic effect was fabricated through drop-casting and drying silver nanowires and graphene films on polydimethylsiloxane substrates and was further successfully applied for continuous monitoring of BP. This strain sensor exhibited ultrahigh sensitivity with a maximum gauge factor of 34357.2 (∼700% sensitivity enhancement over other major sensors), satisfactory response time (∼85 ms), wide strange range (12%), and excellent stability. An interlayer fracture mechanism was proposed to elucidate the working principle of the strain sensor. The real-time BP values can be obtained by analyzing the relationship between the BP and the pulse transit time. To verify our strain sensor for real-time BP monitoring, our strain sensor was compared with a conventional electrocardiogram-photoplethysmograph method and a commercial cuff-based device and showed similar measurement results to BP values from both methods, with only minor differences of 0.693, 0.073, and 0.566 mmHg in the systolic BP, diastolic BP, and mean arterial pressure, respectively. Furthermore, the reliability of the strain sensors was validated by testing 20 human subjects for more than 50 min. This ultrasensitive strain sensor provides a new pathway for continuous and noninvasive BP monitoring.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Flexible Pressure Sensors in Human-Machine Interface Applications

Flexible sensors are highly flexible, malleable, and capable of adapting todifferent shapes, surfaces, and environments, which opens a wide range ofpotential applications in the field of human-machine interface (HMI). Inparticular, flexible pressure sensors as a crucial member of the flexiblesensor family, are widely used in wearable devices, health monitoringinstruments, robots and other fields because they can achieve accuratemeasurement and convert the pressure into electrical signals. The mostintuitive feeling that flexible sensors bring to people is the change ofhuman-machine interface interaction, from the previous rigid interaction suchas keyboard and mouse to flexible interaction such as smart gloves, more inline with people's natural control habits. Many advanced flexible pressuresensors have emerged through extensive research and development, and to adaptto various fields of application. Researchers have been seeking to enhanceperformance of flexible pressure sensors through improving materials, sensingmechanisms, fabrication methods, and microstructures. This paper reviews the flexible pressure sensors in HMI in recent years, mainlyincluding the following aspects: current cutting-edge flexible pressuresensors; sensing mechanisms, substrate materials and active materials; sensorfabrication, performances, and their optimization methods; the flexiblepressure sensors for various HMI applications and their prospects.

High-Sensitivity Flexible Capacitive Pressure Sensors Based on Biomimetic Hibiscus Flower Microstructures

The integration of low-dimensional nanomaterials with microscale architectures in flexible pressure sensors has garnered significant interest due to their outstanding performance in healthcare monitoring. However, achieving high sensitivity across different magnitudes of external pressure remains a critical challenge. Herein, we present a high-performance flexible pressure sensor crafted from biomimetic hibiscus flower microstructures coated with silver nanowires. When compared with a flat electrode, these microstructures as electrodes display significantly enhanced sensitivity and an extended stimulus-response range. Furthermore, we utilized an ionic gel film as the dielectric layer, resulting in an enhancement of the overall performance of the flexible pressure sensor through an increase in interfacial capacitance. Consequently, the capacitive pressure sensor exhibits an extraordinary ultrahigh sensitivity of 48.57 [Kpa]-1 within the pressure range of 0-1 Kpa, 15.24 [Kpa]-1 within the pressure range of 1-30 Kpa, and 3.74 [Kpa]-1 within the pressure range of 30-120 Kpa, accompanied by a rapid response time (<58 ms). The exceptional performance of our flexible pressure sensor serves as a foundation for its numerous applications in healthcare monitoring. Notably, the flexible pressure sensor excels not only in detecting subtle physiological signals such as finger and wrist pulse signals, vocal cord vibrations, and breathing intensity but also demonstrates excellent performance in monitoring higher pressures, such as plantar pressure. We foresee that this flexible pressure sensor possesses significant potential in the field of wearable electronics.

High Signal to Noise Ratio Piezoelectric Thin Film Sensor Based on Elastomer Amplification for Ambulatory Blood Pressure Monitoring

Continuous pulse wave detection can be used for monitoring and diagnosing cardiovascular diseases, and research on pulse sensing based on piezoelectric thin films is one of the hot spots. Usually, piezoelectric thin films do not come into direct contact with the skin and need to be connected through a layer of an elastic medium. Most views think that the main function of this layer of elastic medium is to increase the adhesion between the sensor component and the skin, but there is little discussion about the impact of the elastic medium on pulse vibration transmission. Here, we conducted a detailed study on the effects of Young's modulus and the thickness of elastic media on pulse sensing signals. The results show that the waveform amplitude of the piezoelectric sensing signal decreases with the increase of Young's modulus and thickness of the elastic medium. Then, we constructed a theoretical model of the influence of elastic media on pulse wave propagation. The amplitude of the pulse wave signal detected by the optimized sensor was increased to 480%. Our research shows that by regulating Young's modulus and thickness of elastic media, pulse wave signals can undergo a similar amplification effect, which has an important theoretical reference value for achieving ambulatory blood pressure monitoring based on high-quality pulse waves.

Update on the Use of Pulse Wave Velocity to Measure Age-Related Vascular Changes

PURPOSE OF REVIEW

Pulse wave velocity (PWV) is an important and well-established measure of arterial stiffness that is strongly associated with aging. Age-related alterations in the elastic properties and integrity of arterial walls can lead to cardiovascular disease. PWV measurements play an important role in the early detection of these changes, as well as other cardiovascular disease risk factors, such as hypertension. This review provides a comprehensive summary of the current knowledge of the effects of aging on arterial stiffness, as measured by PWV.

RECENT FINDINGS

This review highlights recent findings showing the applicability of PWV analysis for investigating heart failure, hypertension, and other cardiovascular diseases, as well as cerebrovascular diseases and Alzheimer's disease. It also discusses the clinical implications of utilizing PWV to monitor treatment outcomes, various challenges in implementing PWV assessment in clinical practice, and the development of new technologies, including machine learning and artificial intelligence, which may improve the usefulness of PWV measurements in the future. Measuring arterial stiffness through PWV remains an important technique to study aging, especially as the technology continues to evolve. There is a clear need to leverage PWV to identify interventions that mitigate age-related increases in PWV, potentially improving CVD outcomes and promoting healthy vascular aging.

Objective Evaluation of Pulse Width Using an Array Pulse Diagram

BACKGROUND

Pulse width, which can reflect qi, blood excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis.

METHODS

The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability.

RESULTS

The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater.

CONCLUSIONS

The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies

Background/objective

Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy.

Methods

A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring.

Findings

Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies.

Conclusion

This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Development and clinical validation of a miniaturized finger probe for bedside hemodynamic monitoring

Our aim is to develop a blood pressure (BP) measurement technology that could be integrated into a finger-worn pulse oximeter, eliminating the need for a brachial cuff. We present a miniature cuffless tonometric finger probe system that uses the oscillometric method to measure BP. Our approach uses a motorized press that is used to apply pressure to the fingertip to measure BP. We verified the functionality of the device in a clinical trial (n = 43) resulting in systolic and diastolic pressures ((mean ± SD) mmHg) of (-3.5 ± 8.4) mmHg and (-4.0 ± 4.4) mmHg, respectively. Comparison was made with manual auscultation (n = 26) and automated cuff oscillometry (n = 18). In addition to BP, we demonstrated the ability of the device to assess arterial stiffness (n = 18) and detect atrial fibrillation (n = 6). We were able to introduce a sufficiently small device that could be used for convenient ambulatory measurements with minimal discomfort.

Recent advances of additively manufactured noninvasive kinematic biosensors

The necessity of reliable measurement data assessment in the realm of human life has experienced exponential growth due to its extensive utilization in health monitoring, rehabilitation, surgery, and long-term treatment. As a result, the significance of kinematic biosensors has substantially increased across various domains, including wearable devices, human-machine interaction, and bioengineering. Traditionally, the fabrication of skin-mounted biosensors involved complex and costly processes such as lithography and deposition, which required extensive preparation. However, the advent of additive manufacturing has revolutionized biosensor production by facilitating customized manufacturing, expedited processes, and streamlined fabrication. AM technology enables the development of highly sensitive biosensors capable of measuring a wide range of kinematic signals while maintaining a low-cost aspect. This paper provides a comprehensive overview of state-of-the-art noninvasive kinematic biosensors created using diverse AM technologies. The detailed development process and the specifics of different types of kinematic biosensors are also discussed. Unlike previous review articles that primarily focused on the applications of additively manufactured sensors based on their sensing data, this article adopts a unique approach by categorizing and describing their applications according to their sensing frequencies. Although AM technology has opened new possibilities for biosensor fabrication, the field still faces several challenges that need to be addressed. Consequently, this paper also outlines these challenges and provides an overview of future applications in the field. This review article offers researchers in academia and industry a comprehensive overview of the innovative opportunities presented by kinematic biosensors fabricated through additive manufacturing technologies.

Perspectives in Wearable Systems in the Human-Robot Interaction (HRI) Field

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human-machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

Microfluidic Wearable Devices for Sports Applications

This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.

Particle Counting Methods Based on Microfluidic Devices

Particle counting serves as a pivotal constituent in diverse analytical domains, encompassing a broad spectrum of entities, ranging from blood cells and bacteria to viruses, droplets, bubbles, wear debris, and magnetic beads. Recent epochs have witnessed remarkable progressions in microfluidic chip technology, culminating in the proliferation and maturation of microfluidic chip-based particle counting methodologies. This paper undertakes a taxonomical elucidation of microfluidic chip-based particle counters based on the physical parameters they detect. These particle counters are classified into three categories: optical-based counters, electrical-based particle counters, and other counters. Within each category, subcategories are established to consider structural differences. Each type of counter is described not only in terms of its working principle but also the methods employed to enhance sensitivity and throughput. Additionally, an analysis of future trends related to each counter type is provided.

A Review of Epidermal Flexible Pressure Sensing Arrays

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.

Electromechanically Functionalized Ureteral Stents for Wireless Obstruction Monitoring

While millions of ureteral stents are placed in patients with urinary tract issues around the world every year, hydronephrosis still poses great danger to these patients as a common complication. In the present work, an intelligent double-J ureteral stent equipped with a micro pressure sensor and antenna circuitry is investigated and prototyped toward enabling continuous wireless monitoring of kidney pressure to detect a ureteral obstruction and the resultant hydronephrosis via the indwelling stent. This electromechanically functionalized "intelligent" ureteral stent acts as a radiofrequency resonator with a pressure-sensitive resonant frequency that can be interrogated using an external antenna to track the local pressure. The prototype passes mechanical bending tests of up to 15 cm radius of curvature and shows wireless sensing with a sensitivity of 3.1 kHz/mmHg in artificial urine, which represents 25× enhancement over the preceding design, using an in vitro model with test tissue layers and a pressure range that functions within the conditions found in hydronephrotic conditions. These promising results are expected to propel intelligent ureteral stent technology into further clinical research.

Molecular-electromechanical system for unamplified detection of trace analytes in biofluids

Biological research and diagnostic applications normally require analysis of trace analytes in biofluids. Although considerable advancements have been made in developing precise molecular assays, the trade-off between sensitivity and ability to resist non-specific adsorption remains a challenge. Here, we describe the implementation of a testing platform based on a molecular-electromechanical system (MolEMS) immobilized on graphene field-effect transistors. A MolEMS is a self-assembled DNA nanostructure, containing a stiff tetrahedral base and a flexible single-stranded DNA cantilever. Electromechanical actuation of the cantilever modulates sensing events close to the transistor channel, improving signal-transduction efficiency, while the stiff base prevents non-specific adsorption of background molecules present in biofluids. A MolEMS realizes unamplified detection of proteins, ions, small molecules and nucleic acids within minutes and has a limit of detection of several copies in 100 μl of testing solution, offering an assay methodology with wide-ranging applications. In this protocol, we provide step-by-step procedures for MolEMS design and assemblage, sensor manufacture and operation of a MolEMS in several applications. We also describe adaptations to construct a portable detection platform. It takes ~18 h to construct the device and ~4 min to finish the testing from sample addition to result.

An ultralow power wearable vital sign sensor using an electromagnetically reactive near field

Despite coronavirus disease 2019, cardiovascular disease, the leading cause of global death, requires timely detection and treatment for a high survival rate, underscoring the 24 h monitoring of vital signs. Therefore, telehealth using wearable devices with vital sign sensors is not only a fundamental response against the pandemic but a solution to provide prompt healthcare for the patients in remote sites. Former technologies which measured a couple of vital signs had features that disturbed practical applications to wearable devices, such as heavy power consumption. Here, we suggest an ultralow power (100 μW) sensor that collects all cardiopulmonary vital signs, including blood pressure, heart rate, and the respiration signal. The small and lightweight (2 g) sensor designed to be easily embedded in the flexible wristband generates an electromagnetically reactive near field to monitor the contraction and relaxation of the radial artery. The proposed ultralow power sensor measuring noninvasively continuous and accurate cardiopulmonary vital signs at once will be one of the most promising sensors for wearable devices to bring telehealth to our lives.

Role of Nanomaterials in the Fabrication of bioNEMS/MEMS for Biomedical Applications and towards Pioneering Food Waste Utilisation

bioNEMS/MEMS has emerged as an innovative technology for the miniaturisation of biomedical devices with high precision and rapid processing since its first R&D breakthrough in the 1980s. To date, several organic including food waste derived nanomaterials and inorganic nanomaterials (e.g., carbon nanotubes, graphene, silica, gold, and magnetic nanoparticles) have steered the development of high-throughput and sensitive bioNEMS/MEMS-based biosensors, actuator systems, drug delivery systems and implantable/wearable sensors with desirable biomedical properties. Turning food waste into valuable nanomaterials is potential groundbreaking research in this growing field of bioMEMS/NEMS. This review aspires to communicate recent progress in organic and inorganic nanomaterials based bioNEMS/MEMS for biomedical applications, comprehensively discussing nanomaterials criteria and their prospects as ideal tools for biomedical devices. We discuss clinical applications for diagnostic, monitoring, and therapeutic applications as well as the technological potential for cell manipulation (i.e., sorting, separation, and patterning technology). In addition, current in vitro and in vivo assessments of promising nanomaterials-based biomedical devices will be discussed in this review. Finally, this review also looked at the most recent state-of-the-art knowledge on Internet of Things (IoT) applications such as nanosensors, nanoantennas, nanoprocessors, and nanobattery.

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa^-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Electrospinning-Based Biosensors for Health Monitoring

In recent years, many different biosensors are being used to monitor physical health. Electrospun nanofiber materials have the advantages of high specific surface area, large porosity and simple operation. These properties play a vital role in biosensors. However, the mechanical properties of electrospun nanofibers are poor relative to other techniques of nanofiber production. At the same time, the organic solvents used in electrospinning are generally toxic and expensive. Meanwhile, the excellent performance of electrospun nanofibers brings about higher levels of sensitivity and detection range of biosensors. This paper summarizes the principle and application of electrospinning technology in biosensors and its comparison with other technologies.

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.

Functionality of Flexible Pressure Sensors in Cardiovascular Health Monitoring: A Review

As the highest percentage of global mortality is caused by several cardiovascular diseases (CVD), maintenance and monitoring of a healthy cardiovascular condition have become the primary concern of each and every individual. Simultaneously, recent progress and advances in wearable pressure sensor technology have provided many pathways to monitor and detect underlying cardiovascular illness in terms of irregularities in heart rate, blood pressure, and blood oxygen saturation. These pressure sensors can be comfortably attached onto human skin or can be implanted on the surface of vascular grafts for uninterrupted monitoring of arterial blood pressure. While the traditional monitoring systems are time-consuming, expensive, and not user-friendly, flexible sensor technology has emerged as a promising and dynamic practice to collect important health information at a comparatively low cost in a reliable and user-friendly way. This Review explores the importance and necessity of cardiovascular health monitoring while emphasizing the role of flexible pressure sensors in monitoring patients' health conditions to avoid adverse effects. A comprehensive discussion on the current research progress along with the real-time impact and accessibility of pressure sensors developed for cardiovascular health monitoring applications has been provided.

Noncontact Human-Machine Interface Using Complementary Information Fusion Based on MEMS and Triboelectric Sensors

Current noncontact human-machine interfaces (HMIs) either suffer from high power consumption, complex signal processing circuits, and algorithms, or cannot support multidimensional interaction. Here, a minimalist, low-power, and multimodal noncontact interaction interface is realized by fusing the complementary information obtained from a microelectromechanical system (MEMS) humidity sensor and a triboelectric sensor. The humidity sensor composed of a two-port aluminum nitride (AlN) bulk wave resonator operating in its length extensional mode and a layer of graphene oxide (GO) film with uniform and controllable thickness, possesses an ultra-tiny form factor (200 × 400 µm² ), high signal strength (Q = 1729.5), and low signal noise level (±0.31%RH), and is able to continuously and steadily interact with an approaching finger. Meanwhile, the facile triboelectric sensor made of two annular aluminum electrodes enables the interaction interface to rapidly recognize the multidirectional finger movements. By leveraging the resonant frequency changes of the humidity sensor and output voltage waveforms of the triboelectric sensor, the proposed interaction interface is successfully demonstrated as a game control interface to manipulate a car in virtual reality (VR) space and a password input interface to enter high-security 3D passwords, indicating its great potential in diversified applications in the future Metaverse.

Flexible Wearable Pressure Sensor Based on Collagen Fiber Material

Flexible wearable pressure sensors play a pivotal role in healthcare monitoring, disease prevention, and humanmachine interactions. However, their narrow sensing ranges, low detection sensitivities, slow responses, and complex preparation processes restrict their application in smart wearable devices. Herein, a capacitive pressure sensor with high sensitivity and flexibility that uses an ionic collagen fiber material as the dielectric layer is proposed. The sensor exhibits a high sensitivity (5.24 kPa−1), fast response time (40 ms), long-term stability, and excellent repeatability over 3000 cycles. Because the sensor is resizable, flexible, and has a simple preparation process, it can be flexibly attached to clothes and the human body for wearable monitoring. Furthermore, the practicality of the sensor is proven by attaching it to different measurement positions on the human body to monitor the activity signal.

Frequency Characteristics of Pulse Wave Sensor Using MEMS Piezoresistive Cantilever Element

Wearable sensor devices with minimal discomfort to the wearer have been widely developed to realize continuous measurements of vital signs (body temperature, blood pressure, respiration rate, and pulse wave) in many applications across various fields, such as healthcare and sports. Among them, microelectromechanical systems (MEMS)-based differential pressure sensors have garnered attention as a tool for measuring pulse waves with weak skin tightening. Using a MEMS-based piezoresistive cantilever with an air chamber as the pressure change sensor enables highly sensitive pulse-wave measurements to be achieved. Furthermore, the initial static pressure when attaching the sensor to the skin is physically excluded because of air leakage around the cantilever, which serves as a high-pass filter. However, if the frequency characteristics of this mechanical high-pass filter are not appropriately designed, then the essential information of the pulse-wave measurement may not be reflected. In this study, the frequency characteristics of a sensor structure is derived theoretically based on the air leakage rate and chamber size. Subsequently, a pulse wave sensor with a MEMS piezoresistive cantilever element, two air chambers, and a skin-contacted membrane is designed and fabricated. The developed sensor is 30 mm in diameter and 8 mm in thickness and realizes high-pass filter characteristics of 0.7 Hz. Finally, pulse wave measurement at the neck of a participant is demonstrated using the developed sensor. It is confirmed that the measured pulse wave contains signals in the designed frequency band.

A self-powered wireless motion sensor based on a high-surface area reverse electrowetting-on-dielectric energy harvester

This paper presents a motion-sensing device with the capability of harvesting energy from low-frequency motion activities. Based on the high surface area reverse electrowetting-on-dielectric (REWOD) energy harvesting technique, mechanical modulation of the liquid generates an AC signal, which is modeled analytically and implemented in Matlab and COMSOL. A constant DC voltage is produced by using a rectifier and a DC-DC converter to power up the motion-sensing read-out circuit. A charge amplifier converts the generated charge into a proportional output voltage, which is transmitted wirelessly to a remote receiver. The harvested DC voltage after the rectifier and DC-DC converter is found to be 3.3 V, having a measured power conversion efficiency (PCE) of the rectifier as high as 40.26% at 5 Hz frequency. The energy harvester demonstrates a linear relationship between the frequency of motion and the generated output power, making it highly suitable as a self-powered wearable motion sensor.

Revolution in Flexible Wearable Electronics for Temperature and Pressure Monitoring—A Review

In the last few decades, technology innovation has had a huge influence on our lives and well-being. Various factors of observing our physiological characteristics are taken into account. Wearable sensing tools are one of the most imperative sectors that are now trending and are expected to grow significantly in the coming days. Externally utilized tools connected to any human to assess physiological characteristics of interest are known as wearable sensors. Wearable sensors range in size from tiny to large tools that are physically affixed to the user and operate on wired or wireless terms. With increasing technological capabilities and a greater grasp of current research procedures, the usage of wearable sensors has a brighter future. In this review paper, the recent developments of two important types of wearable electronics apparatuses have been discussed for temperature and pressure sensing (Psensing) applications. Temperature sensing (Tsensing) is one of the most important physiological factors for determining human body temperature, with a focus on patients with long-term chronic conditions, normally healthy, unconscious, and injured patients receiving surgical treatment, as well as the health of medical personnel. Flexile Psensing devices are classified into three categories established on their transduction mechanisms: piezoresistive, capacitive, and piezoelectric. Many efforts have been made to enhance the characteristics of the flexible Psensing devices established on these mechanisms.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the "guan" location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann-Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APV h1, APV h3, APV h4, APV h3/h1 (P < 0.01), and APV h4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APV h4, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) and the mean APV h3, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APV h4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

Bioinspired Self-Powered Piezoresistive Sensors for Simultaneous Monitoring of Human Health and Outdoor UV Light Intensity

The exact fabrication of precise three-dimensional structures for piezoresistive sensors necessitates superior manufacturing methods or tooling, which are accompanied by time-consuming processes and the potential for environmental harm. Herein, we demonstrated a method for in situ synthesis of zinc oxide nanorod (ZnO NR) arrays on graphene-treated cotton and paper substrates and constructed highly sensitive, flexible, wearable, and chemically stable strain sensors. Based on the structure of pine trees and needles in nature, the hybrid sensing layer consisted of graphene-attached cotton or paper fibers and ZnO NRs, and the results showed a high sensitivity of 0.389, 0.095, and 0.029 kPa^-1 and an ultra-wide linear range of 0-100 kPa of this sensor under optimal conditions. Our study found that water absorption and swelling of graphene fibers and the associated reduction of pore size and growth of zinc oxide were detrimental to pressure sensor performance. A random line model was developed to examine the effects of different hydrothermal times on sensor performance. Meanwhile, pulse detection, respiration detection, speech recognition, and motion detection, including finger movements, walking, and throat movements, were used to show their practical application in human health activity monitoring. In addition, monolithically grown ZnO NRs on graphene cotton sheets had been integrated into a flexible sensing platform for outdoor UV photo-indication, which is, to our knowledge, the first successful case of an integrated UV photo-detector and motion sensor. Due to its excellent strain detection and UV detection abilities, these strategies are a step forward in developing wearable sensors that are cost-controllable and high-performance.

Spatiotemporal Measurement of Arterial Pulse Waves Enabled by Wearable Active-Matrix Pressure Sensor Arrays

Wearable pressure sensors have demonstrated great potential in detecting pulse pressure waves on the skin for the noninvasive and continuous diagnosis of cardiac conditions. However, difficulties lie in positioning conventional single-point sensors on an invisible arterial line, thereby preventing the detection of adequate signal amplitude for accurate pulse wave analysis. Herein, we introduce the spatiotemporal measurements of arterial pulse waves using wearable active-matrix pressure sensors to obtain optimal pulse waveforms. We fabricate thin-film transistor (TFT) arrays with high yield and uniformity using inkjet printing where array sizes can be customizable and integrate them with highly sensitive piezoresistive sheets. We maximize the pressure sensitivity (16.8 kPa^-1) and achieve low power consumption (10¹ nW) simultaneously by strategically modulating the TFT operation voltage. The sensor array creates a spatiotemporal pulse wave map on the wrist. The map presents the positional dependence of pulse amplitudes, which allows the positioning of the arterial line to accurately extract the augmentation index, a parameter for assessing arterial stiffness. The device overcomes the positional inaccuracy of conventional single-point sensors, and therefore, it can be used for medical applications such as arterial catheter injection or the diagnosis of cardiovascular disease in daily life.

Microelectromechanical Systems (MEMS) for Biomedical Applications

The significant advancements within the electronics miniaturization field have shifted the scientific interest towards a new class of precision devices, namely microelectromechanical systems (MEMS). Specifically, MEMS refers to microscaled precision devices generally produced through micromachining techniques that combine mechanical and electrical components for fulfilling tasks normally carried out by macroscopic systems. Although their presence is found throughout all the aspects of daily life, recent years have witnessed countless research works involving the application of MEMS within the biomedical field, especially in drug synthesis and delivery, microsurgery, microtherapy, diagnostics and prevention, artificial organs, genome synthesis and sequencing, and cell manipulation and characterization. Their tremendous potential resides in the advantages offered by their reduced size, including ease of integration, lightweight, low power consumption, high resonance frequency, the possibility of integration with electrical or electronic circuits, reduced fabrication costs due to high mass production, and high accuracy, sensitivity, and throughput. In this context, this paper aims to provide an overview of MEMS technology by describing the main materials and fabrication techniques for manufacturing purposes and their most common biomedical applications, which have evolved in the past years.

Recent Advances in Materials and Flexible Sensors for Arrhythmia Detection

Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.

Overload performance study and fabrication of the capacitive pressure-sensitive chip with linkage film

In order to satisfy the demands of pressure measurement under high overload conditions, the overload characteristics of the capacitive pressure-sensitive chip with linkage film are studied. Through the simulation and analysis for the stress distribution of this sensitive structure and in light of the dimension effect of the tensile strength of monocrystalline silicon, the influence of sizes of the sensitive structure on the overload pressure is expounded. The simulated results illustrate that the maximum overload pressure can exceed 200 times the full-scale (FS) when appropriately adjusting the sizes of the sensitive structure. The proposed chip is fabricated using SOI wafers combined with bonding technology. Our experimental results show that the sample chip has a linear response in the pressure range of 25-100 kPa, and its overload pressure is 4.5 MPa, reaching 45 times the full-scale.

A First Step towards a Comprehensive Approach to Harmonic Analysis of Synchronous Peripheral Volume Pulses: A Proof-of-Concept Study

The harmonic analysis (HA) of arterial radial pulses in humans has been widely investigated in recent years for clinical applications of traditional Chinese medicine. This study aimed at establishing the validity of carrying out HA on synchronous peripheral volume pulses for predicting diabetes-induced subtle changes in heart energy. In this study, 141 subjects (Group 1: 63 healthy elderly subjects; Group 2: 78 diabetic subjects) were enrolled at the same hospital. After routine blood sampling, all synchronous electrocardiogram (ECG) and photoplethysmography (PPG) measurements (i.e., at the six locations) were acquired in the morning. HA of synchronous peripheral volume pulses and radial pulse waves was performed and analyzed after a short period of an ensemble averaging process based on the R-wave peak location. This study utilized HA for the peripheral volume pulses and found that the averaged total pulse energy (i.e., the C₀ of the DTFS) was identical in the same subject. A logistic regression model with C₀ and a waist circumference variable showed a graded association with the risk of developing type 2 diabetes. The adjusted odds ratio for C₀ and the waist circumference were 0.986 (95% confidence interval: 0.977, 0.994) and 1.130 (95% confidence interval: 1.045, 1.222), respectively. C₀ also showed significant negative correlations with risk factors for type 2 diabetes mellitus, including glycosylated hemoglobin and fasting plasma glucose (r = −0.438, p < 0.001; r = −0.358, p < 0.001, respectively). This study established a new application of harmonic analysis in synchronous peripheral volume pulses for clinical applications. The findings showed that the C₀ could be used as a prognostic indicator of a protective factor for predicting type 2 diabetes.

Clinical Validation of a Soft Wireless Continuous Blood Pressure Sensor During Surgery

We test a new wireless soft capacitance sensor (CAP) based on applanation tonometry at the radial and dorsalis pedis arteries against the gold standard, invasive arterial line (A-Line), for continuous beat to beat blood pressure (BP) measurements in the Operating Room during surgical procedures under anesthesia in 17 subjects with the mean age and body mass index (BMI) of 57. 35 ± 18.72 years and 27.36 ± 4.20 kg/m², respectively. We have identified several parameters to monitor in order to compare how well the CAP sensor tracks the entire hemodynamic waveform as compared to the A-Line. This includes waveform similarity, heart rate (HR), absolute systolic BP (SBP), diastolic BP (DBP), and temporal response to a vasopressor. Overall, the CAP sensor shows good correlations with A-Line with respect to hemodynamic shape (r > 0.89), HR (mean bias = 0.0006; SD = 0.17), absolute SBP, and DBP in a line of best fit (slope = 0.98 in SBP; 1.08 in DBP) and the mean bias derived from Bland-Altman method to be 1.92 (SD = 12.55) in SBP and 2.38 (SD = 12.19) in DBP across body habitus and age in OR patients under general anesthesia. While we do observe drifts in the system, we still obtain decent correlations with respect to the A-Line as evidenced by excellent linear fit and low mean bias across patients. When we post-process using a different calibration method to account for the drift, the mean bias and SD improve dramatically to −1.85 and 7.19 DBP as well as 1.43 and 7.43 SBP, respectively, indicating a promising potential for improvement when we integrate strategies to account for movement identified by our integrated accelerometer data.

A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

Hypertension assessment based on feature extraction using a photoplethysmography signal and its derivatives

Objective.Long-term abnormal blood pressure (BP) can lead to various cardiovascular diseases; therefore, it is significant to assess BP status as a preventative measure. In this study, a feature-extraction-based approach is proposed and performed on an open clinical trial dataset.Approach.Firstly, a complete ensemble of empirical mode decomposition with an adaptive noise algorithm and wavelet threshold analysis is applied to eliminate the noise interference from an original photoplethysmography (PPG) signal compared to other signal filters. Considering the strong connection between hypertension and diabetes, an analysis of variance test with a 95% confidence interval is firstly carried out to select these leading extracted morphological features, which are uniquely related to hypertension, from the PPG signal and its derivatives. Subsequently a variety of classification models are evaluated at different BP levels and their performances are compared.Main results and Significance.The test results demonstrate that the support vector machine classification model achieves a greater performance compared to other explored models in this paper, with accuracy of 78%, 87% and 88% for cases including normal versus prehypertension subjects, normotension versus hypertension subjects and non-hypertension versus hypertension subjects, respectively, which further illustrates the great potential of the proposed method in hypertension assessment.

Portable Interactive Pulse Tactile Recorder and Player System

Pulse palpation is an effective method for diagnosing arterial diseases. However, most pulse measurement devices use preconfigured pressures to collect pulse signals, and most pulse tactile simulators can only display standard or predefined pulse waveforms. Here, a portable interactive human pulse measurement and reproduction system was developed that allows users to take arbitrary pulses and experience realistic simulated pulse tactile feedback in real time by using their natural pulse-taking behaviors. The system includes a pulse tactile recorder and a pulse tactile player. Pulse palpation forces and vibrations can be recorded and realistically replayed for later tactile exploration and examination. To retain subtle but vital pulse information, empirical mode decomposition was used to decompose pulse waveforms into several intrinsic mode functions. Artificial neural networks were then trained based on intrinsic mode functions to determine the relationship between the driving signals of the pulse tactile player and the resulting vibration waveforms. Experimental results indicate that the average normalized root mean square error and the average R-squared values between the reproduced and original pulses were 0.0654 and 0.958 respectively, which indicate that the system can reproduce high-fidelity pulse tactile vibrations.

Non-Invasive Methods for PWV Measurement in Blood Vessel Stiffness Assessment

In recent years, statistical studies highlighted an increasing incidence of cardiovascular diseases (CVD) which reflected on additional costs on the healthcare systems worldwide. Pulse wave velocity (PWV) measurement is commonly considered a CVD predictor factor as well as a marker of Arterial Stiffness (AS), since it is closely related to the mechanical characteristics of the arterial wall. An increase in PWV is due to a more rigid arterial system. Because of the prevalence of the elastic component, in young people the PWV is lower than in the elderly. Nowadays, invasive and non-invasive methods for PWV assessment are employed: there is an increasing attention in the development of non-invasive devices which mostly perform a regional PWV measurement (over a long arterial portion) rather than local (over a short arterial portion). The accepted gold-standard for non-invasive AS measurement is the carotid-femoral PWV used to evaluate the arterial damage, the corresponding cardiovascular risk and to adapt the proper therapy. This review article considers the main commercially available devices underlining their operating principles in terms of sensors, execution mode, pulse waveform acquired, site of measurement, distance and time estimation methods, as well as their main limitations in clinical practice.

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

The underlying mechanism of intersite discrepancies in ejection time measurements from arterial waveforms and its validation in the Framingham Heart Study

Radial applanation tonometry is a well-established method for clinical hemodynamic assessment and is also becoming popular in wrist-worn fitness trackers. The time difference between the foot and the dicrotic notch of the arterial pressure waveform is a well-accepted approximation for the left ventricular ejection time (ET). However, several clinical studies have shown that ET measured from the radial pressure waveform deviates from that measured centrally. In this work, we consider the systolic wave and the dicrotic wave as two independent traveling waves and hypothesize that their wave speed difference leads to the intersite differences of measured ET (ΔET). Accordingly, we derived a mathematical dicrotic wave decomposition model and identified the most influential factors on ΔET via global sensitivity analysis. In our clinical validation on a heterogeneous cohort (N = 5,742) from the Framingham Heart Study (FHS), the local sensitivity analysis results resembled the sensitivity variation patterns of ΔET from model simulations. A regression analysis on FHS data, using morphological features of radial pressure waveforms to estimate the carotid ET, produced a root mean square error of 3.76 ms and R² of 0.91. The proposed dicrotic wave decomposition model can explain the intersite ET measurement discrepancies observed in the clinical data of FHS and can facilitate the precise identification of ET with radial pressure waveforms. Therefore, the proposed model will improve various physics-based pulse wave analysis methods as well as prospective artificial intelligence methods for tackling the subsequent big data produced from widespread wearable radial pressure monitoring.NEW & NOTEWORTHY Based on a new understanding of pressure wave propagation, we propose a novel dicrotic wave decomposition model considering the dicrotic wave as an independent traveling component. The proposed model can explain the mechanism underlying the intersite discrepancies in ejection time measurement from arterial waveforms and then, in principle, enhance the accuracy of both classical physics-based as well as more contemporary artificial intelligence-based pulse wave analysis methods in clinical and wearable radial blood pressure monitoring applications.

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays

In this study, we developed a radial artery pulse acquisition system based on finger-worn dense pressure sensor arrays to enable three-dimensional pulse signals acquisition. The finger-worn dense pressure-sensor arrays were fabricated by packaging 18 ultra-small MEMS pressure sensors (0.4 mm × 0.4 mm × 0.2 mm each) with a pitch of 0.65 mm on flexible printed circuit boards. Pulse signals are measured and recorded simultaneously when traditional Chinese medicine practitioners wear the arrays on the fingers while palpating the radial pulse. Given that the pitches are much smaller than the diameter of the human radial artery, three-dimensional pulse envelope images can be measured with the system, as can the width and the dynamic width of the pulse signals. Furthermore, the array has an effective span of 11.6 mm-3-5 times the diameter of the radial artery-which enables easy and accurate positioning of the sensor array on the radial artery. This study also outlines proposed methods for measuring the pulse width and dynamic pulse width. The dynamic pulse widths of three volunteers were measured, and the dynamic pulse width measurements were consistent with those obtained by color Doppler ultrasound. The pulse wave velocity can also be measured with the system by measuring the pulse transit time between the pulse signals at the brachial and radial arteries using the finger-worn sensor arrays.

Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.