Physiological acoustic sensing based on accelerometers: a survey for mobile healthcare

Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring

Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.

Emerging Trends in Soft Electronics: Integrating Machine Intelligence with Soft Acoustic/Vibration Sensors

In the last decade, soft acoustic/vibration sensors have gained tremendous research interest due to their unique ability to detect broadband acoustic/vibration stimuli, potentializing futuristic applications including voice biometrics, voice-controlled human-machine-interfaces, electronic skin, and skin-mountable healthcare devices. Importantly, to benefit most from these sensors, it is inevitable to use machine learning (ML) to process their output signals; with ML, a more accurate and efficient interpretation of original data is possible. This paper is dedicated to offering an overview of recent advances empowering the development of soft acoustic/vibration sensors and their signal processing using ML. First, the key performance parameters of the sensors are discussed. Second, popular transduction mechanisms for the sensors are addressed, followed by an in-depth overview of each type, covering materials used, structural designs, and sensing performances. Third, potential applications of the sensors are elaborated and fourth, a thorough discussion on ML is conducted, exploring different types of ML, specific ML algorithms suitable for processing acoustic/vibration signals, and current trends in ML-assisted applications. Finally, the challenges and potential opportunities in soft acoustic/vibration sensor and ML research are revealed to offer new insights into future prospects in these fields.

Margined Horn-Shaped Air Chamber for Body-Conduction Microphone

The sound amplification ratios of sealed air chambers with different shapes were quantitatively compared to design a body-conduction microphone to measure animal scratching sounds. Recently, quantitative monitoring of scratching intensity in dogs has been required. We have already developed a collar with a body-conduction microphone to measure body-conducted scratching sounds. However, the air chamber, one of the components of the body-conduction microphone, has not been appropriately designed. This study compared the amplification ratios of air chambers with different shapes through numerical analysis and experiments. According to the results, the horn-shaped air chamber achieved the highest amplification performance, at least for sound frequencies below 3 kHz. The simulated amplification ratio of the horn-shaped air chamber with a 1 mm height and a 15 mm diameter was 52.5 dB. The deformation of the bottom of the air chamber affected the amplification ratio. Adjusting the margin of the margined horn shape could maintain its amplification ratio at any pressing force. The simulated and experimental amplification ratios of the margined horn-shaped air chamber were 53.4 dB and 19.4 dB, respectively.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

A Passive Smart Face Mask for Wireless Cough Monitoring: A Harmonic Detection Scheme With Clutter Rejection

Cough detection has aroused great interest because the assessment of cough frequency may improve diagnosis accuracy for dealing with several diseases, such as chronic obstructive pulmonary disease (COPD) and the recent COVID-19 global pandemic crisis. Here, we propose and experimentally demonstrate a wireless smart face mask based on a passive harmonic tag for real-time cough monitoring and alert. Our results show that the cough events can be successfully monitored through non-contact track of the received signal strength indicator (RSSI) at the harmonic frequency. Owing to the frequency orthogonality between the launched and backscattered radio-frequency (RF) signals, the harmonic tag-based smart mask can well suppress the electromagnetic interferences, such as clutters and crosstalks in noisy environments. We envision that this zero-power and lightweight wireless wearable device may be beneficial for cough monitoring and the public health condition in terms of tracking potential contagious person and virus-transmissive events.

Telemedicine in Cardiology: Modern Technologies to Improve Cardiovascular Patients’ Outcomes—A Narrative Review

The registration of physical signals has long been an important part of cardiological diagnostics. Current technology makes it possible to send large amounts of data to remote locations. Solutions that enable diagnosis and treatment without direct contact with patients are of enormous value, especially during the COVID-19 outbreak, as the elderly require special protection. The most important examples of telemonitoring in cardiology include the use of implanted devices such as pacemakers and defibrillators, as well as wearable sensors and data processing units. The arrythmia detection and monitoring patients with heart failure are the best studied in the clinical setting, although in many instances we still lack clear evidence of benefits of remote approaches vs. standard care. Monitoring for ischemia is less well studied. It is clear however that the economic and organizational gains of telemonitoring for healthcare systems are substantial. Both patients and healthcare professionals have expressed an enormous demand for the further development of such technologies. In addition to these subjects, in this paper we also describe the safety concerns associated with transmitting and storing potentially sensitive personal data.

Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research

Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality despite current treatment strategies which focus on smoking cessation, pulmonary rehabilitation, and symptomatic relief. A focus of COPD care is to encourage self-management, particularly during COVID-19, where much face-to-face care has been reduced or ceased. Digital health solutions may offer affordable and scalable solutions to support COPD patient education and self-management, such solutions could improve clinical outcomes and expand service reach for limited additional cost. However, optimal ways to deliver digital medicine are still in development, and there are a number of important considerations for clinicians, commissioners, and patients to ensure successful implementation of digitally augmented care. In this narrative review, we discuss advantages, pitfalls, and future prospects of digital healthcare, which offer a variety of tools including self-management plans, education videos, inhaler training videos, feedback to patients and healthcare professionals (HCPs), exacerbation monitoring, and pulmonary rehabilitation. We discuss the key issues with sustaining patient and HCP engagement and limiting attrition of use, interoperability with devices, integration into healthcare systems, and ensuring inclusivity and accessibility. We explore the essential areas of research beyond determining safety and efficacy to understand the acceptability of digital healthcare solutions to patients, clinicians, and healthcare systems, and hence ways to improve this and sustain engagement. Finally, we explore the regulatory challenges to ensure quality and engagement and effective integration into current healthcare systems and care pathways, while maintaining patients’ autonomy and privacy. Understanding and addressing these issues and successful incorporation of an acceptable, simple, scalable, affordable, and future-proof digital solution into healthcare systems could help remodel global chronic disease management and fractured healthcare systems to provide best patient care and optimisation of healthcare resources to meet the global burden and unmet clinical need of COPD.

Cardiac monitoring for patients with palpitations

Palpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations.

Emerging Implantable Device Technology for Patients at the Intersection of Electrophysiology and Heart Failure Interdisciplinary Care

Cardiac implantable electronic devices (CIEDs), including implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (CRT), are part of guideline- indicated treatment for a subset of patients with heart failure with reduced ejection fraction (HFrEF). Current technological advancements in CIEDs have allowed the detection of specific patient physiologic parameters used for forecasting clinical decompensation through algorithmic, multiparameter remote monitoring. Other recent emerging technologies, including cardiac contractility modulation (CCM) and baroreflex activation therapy (BAT), may provide symptomatic or physiologic benefit in patients without an indication for CRT. Our goal in this state-of-the-art review is to describe the commercially available new technologies, purported mechanisms of action, evidence surrounding their clinical role, limitations, and future directions. Finally, we underline the need for standardized workflow and close interdisciplinary management of this population to ensure the delivery of high-quality care.

Telemedicine and Remote Monitoring as an Adjunct to Medical Management of Bronchiectasis

The limited resources and the practice of social distancing during the COVID pandemic create a paradigm shift in the utilization of telemedicine in healthcare. However, the implementation of best practices is hampered in part by a lack of literature devoted to telehealth in bronchiectasis. In this commentary, we examine multiple approaches to structuring of telemedicine care for patients with bronchiectasis, highlight current evidence-based interventions that can be incorporated into the management of bronchiectasis, and describe our experience with telemedicine at the University of Connecticut Center for Bronchiectasis Care during the COVID-19 pandemic. The structural model must be adapted to different local dynamics and available technologies with careful attention to patient characteristics and access to technology to avoid the potential paradoxical effects of increasing patients’ burden and healthcare disparities in underserved populations.

A two-stage amplified PZT sensor for monitoring lung and heart sounds in discharged pneumonia patients

Assessment of lung and heart states is of critical importance for patients with pneumonia. In this study, we present a small-sized and ultrasensitive accelerometer for continuous monitoring of lung and heart sounds to evaluate the lung and heart states of patients. Based on two-stage amplification, which consists of an asymmetric gapped cantilever and a charge amplifier, our accelerometer exhibited an extremely high ratio of sensitivity to noise compared with conventional structures. Our sensor achieves a high sensitivity of 9.2 V/g at frequencies less than 1000 Hz, making it suitable to use to monitor weak physiological signals, including heart and lung sounds. For the first time, lung injury, heart injury, and both lung and heart injuries in discharged pneumonia patients were revealed by our sensor device. Our sound sensor also successfully tracked the recovery course of the discharged pneumonia patients. Over time, the lung and heart states of the patients gradually improved after discharge. Our observations were in good agreement with clinical reports. Compared with conventional medical instruments, our sensor device provides rapid and highly sensitive detection of lung and heart sounds, which greatly helps in the evaluation of lung and heart states of pneumonia patients. This sensor provides a cost-effective alternative approach to the diagnosis and prognosis of pneumonia and has the potential for clinical and home-use health monitoring.

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

Fetal movement count monitoring is one of the most commonly used methods of assessing fetal well-being. While few methods are available to monitor fetal movements, they consist of several adverse qualities such as unreliability as well as the inability to be conducted in a non-clinical setting. Therefore, this research was conducted to design a complete system that will enable pregnant mothers to monitor fetal movement at home. This system consists of a non-invasive, non-transmitting sensor unit that can be fabricated at a low cost. An accelerometer was utilized as the primary sensor and a micro-controller based circuit was implemented. Clinical testing was conducted utilizing this sensor unit. Two phases of clinical testing procedures were done and during the first phase readings from 120 mothers were taken while during the second phase readings from 15 mothers were taken. Validation was done by conducting an abdominal ultrasound scan which was utilized as the ground truth during the second phase of the clinical testing procedure. A clinical survey was also conducted in parallel with clinical testings in order to improve the sensor unit as well as to improve the final system. Four different signal processing algorithms were implemented on the data set and the performance of each was compared with each other. Out of the four algorithms three algorithms were able to obtain a true positive rate around 85%. However, the best algorithm was selected on the basis of minimizing the false positive rate. Consequently, the most feasible as well as the best performing algorithm was determined and it was utilized in the final system. This algorithm have a true positive rate of 86% and a false positive rate of 7% Furthermore, a mobile application was also developed to be used with the sensor unit by pregnant mothers. Finally, a complete end to end method to monitor fetal movement in a non-clinical setting was presented by the proposed system.

A Novel Accelerometer Mounting Method for Sensing Performance Improvement in Acoustic Measurements From the Knee

In this study, we propose a new mounting method to improve accelerometer sensing performance in the 50 Hz-10 kHz frequency band for knee sound measurement. The proposed method includes a thin double-sided adhesive tape for mounting and a 3D-printed custom-designed backing prototype. In our mechanical setup with an electrodynamic shaker, the measurements showed a 13 dB increase in the accelerometer's sensing performance in the 1-10 kHz frequency band when it is mounted with the craft tape under 2 N backing force applied through low-friction tape. As a proof-of-concept study, knee sounds of healthy subjects (n = 10) were recorded. When the backing force was applied, we observed statistically significant (p < 0.01) incremental changes in spectral centroid, spectral roll-off frequencies, and high-frequency (1-10 kHz) root-mean-square (RMS) acceleration, while low-frequency (50 Hz-1 kHz) RMS acceleration remained unchanged. The mean spectral centroid and spectral roll-off frequencies increased from 0.8 kHz and 4.15 kHz to 1.35 kHz and 5.9 kHz, respectively. The mean high-frequency acceleration increased from 0.45 mgRMS to 0.9 mgRMS with backing. We showed that the backing force improves the sensing performance of the accelerometer when mounted with the craft tape and the proposed backing prototype. This new method has the potential to be implemented in today's wearable systems to improve the sensing performance of accelerometers in knee sound measurements.

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.

Spider-Inspired Ultrasensitive Flexible Vibration Sensor for Multifunctional Sensing

Flexible vibration sensors can not only capture broad classes of physiologically relevant information, including mechano-vibration signatures of body processes and precision kinematics of core-body motions, but also detect environmental seismic waves, providing early warning to wearers in time. Spider is one of the most vibration-sensitive creatures because of its hairlike sensilla and lyriform slit structure. Here, a spider-inspired ultrasensitive flexible vibration sensor is designed and fabricated for multifunctional sensing. The vibration sensitivity of the flexible sensor is increased over 2 orders of magnitude from 0.006 to 0.5 mV/g, and the strain sensitivity is hugely enhanced from 0.08 to 150 compared to a plain sensor counterpart. It is shown that the synergistic effect of cilium arrays and cracks is the key for achieving the greatly enhanced vibration and strain sensitivity. The dynamic sensitivity of 0.5 mV/g outperforms the corresponding commercial vibration sensors. The flexible sensor is demonstrated to be generally feasible for detecting vibration signals caused by walk, tumble, and explosion as well as capturing human body motions, indicating its great potential for applications in human health-monitoring devices, posture control in robotics, early earthquake warning, and so forth.

Broad Bandwidth, Self-Powered Acoustic Sensor Created by Dynamic Near-Field Electrospinning of Suspended, Transparent Piezoelectric Nanofiber Mesh

Freely suspended nanofibers, such as spider silk, harnessing their small diameter (sub-micrometer) and spanning fiber morphology, behave as a nonresonating acoustic sensor. The associated sensing characteristics, departing from conventional resonant acoustic sensors, could be of tremendous interest for the development of high sensitivity, broadband audible sensors for applications in environmental monitoring, biomedical diagnostics, and internet-of-things. Herein, a low packing density, freely suspended nanofiber mesh with a piezoelectric active polymer is fabricated, demonstrating a self-powered acoustic sensing platform with broad sensitivity bandwidth covering 200-5000 Hz at hearing-safe sound pressure levels. Dynamic near-field electrospinning is developed to fabricate in situ poled poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber mesh (average fiber diameter ≈307 nm), exhibiting visible light transparency greater than 97%. With the ability to span the nanomesh across a suspension distance of 3 mm with minimized fiber stacking (≈18% fiber packing density), individual nanofibers can freely imitate the acoustic-driven fluctuation of airflow in a collective manner, where piezoelectricity is harvested at two-terminal electrodes for direct signal collection. Applications of the nanofiber mesh in music recording with good signal fidelity are demonstrated.

All-nanofiber-based, ultrasensitive, gas-permeable mechanoacoustic sensors for continuous long-term heart monitoring

The prolonged and continuous monitoring of mechanoacoustic heart signals is essential for the early diagnosis of cardiovascular diseases. These bodily acoustics have low intensity and low frequency, and measuring them continuously for long periods requires ultrasensitive, lightweight, gas-permeable mechanoacoustic sensors. Here, we present an all-nanofiber mechanoacoustic sensor, which exhibits a sensitivity as high as 10,050.6 mV Pa-1 in the low-frequency region (<500 Hz). The high sensitivity is achieved by the use of durable and ultrathin (2.5 µm) nanofiber electrode layers enabling a large vibration of the sensor during the application of sound waves. The sensor is ultralightweight, and the overall weight is as small as 5 mg or less. The devices are mechanically robust against bending, and show no degradation in performance even after 1,000-cycle bending. Finally, we demonstrate a continuous long-term (10 h) measurement of heart signals with a signal-to-noise ratio as high as 40.9 decibels (dB).

Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch

Skin-mounted soft electronics incorporating high-bandwidth triaxial accelerometers can provide broad classes of physiologically relevant information, such as mechanoacoustic signatures of underlying body processes (such as those captured by a stethoscope) and precision kinematics of core body motions. Here, we describe a wireless device designed to be conformally placed on the suprasternal notch for the continuous measurement of mechanoacoustic signals, from subtle vibrations of the skin at accelerations of ~10⁻³ m·s⁻² to large motions of the entire body at ~10 m·s⁻², and at frequencies up to ~800 Hz. Because th measurements are a complex superposition of signals that arise from locomotion, body orientation, swallowing, respiration, cardiac activity, vocal-fold vibrations and other sources, we used frequency-domain analysis and machine learning to obtain, from human subjects during natural daily activities and exercise, real-time recordings of heart rate, respiration rate, energy intensity and other essential vital signs, as well as talking time and cadence, swallow counts and patterns, and other unconventional biomarkers. We also used the device in sleep laboratories, and validated the measurements via polysomnography.

Digital technologies and adherence in respiratory diseases: the road ahead

Outcomes for patients with chronic respiratory diseases remain poor despite the development of novel therapies. In part, this reflects the fact that adherence to therapy is low and clinicians lack accurate methods to assess this issue. Digital technologies hold promise to overcome these barriers to care. For example, algorithmic analysis of large amounts of information collected on health status and treatment use, along with other disease relevant information such as environmental data, can be used to help guide personalised interventions that may have a positive health impact, such as establishing habitual and correct inhaler use. Novel approaches to data analysis also offer the possibility of statistical algorithms that are better able to predict exacerbations, thereby creating opportunities for preventive interventions that may adapt therapy as disease activity changes. To realise these possibilities, digital approaches to disease management should be supported by strong evidence, have a solid infrastructure, be designed collaboratively as clinically effective and cost-effective systems, and reflect the needs of patients and healthcare providers. Regulatory standards for digital interventions and strategies to handle the large amounts of data generated are also needed. This review highlights the opportunities provided by digital technologies for managing patients with respiratory diseases.

Acoustic Methods for Pulmonary Diagnosis

Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.

Improved Detection of Lung Fluid With Standardized Acoustic Stimulation of the Chest

Accumulation of excess air and water in the lungs leads to breakdown of respiratory function and is a common cause of patient hospitalization. Compact and non-invasive methods to detect the changes in lung fluid accumulation can allow physicians to assess patients’ respiratory conditions. In this paper, an acoustic transducer and a digital stethoscope system are proposed as a targeted solution for this clinical need. Alterations in the structure of the lungs lead to measurable changes which can be used to assess lung pathology. We standardize this procedure by sending a controlled signal through the lungs of six healthy subjects and six patients with lung disease. We extract mel-frequency cepstral coefficients and spectroid audio features, commonly used in classification for music retrieval, to characterize subjects as healthy or diseased. Using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document}-nearest neighbors algorithm, we demonstrate 91.7% accuracy in distinguishing between healthy subjects and patients with lung pathology.

Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals

Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.

Performance of a wearable acoustic system for fetal movement discrimination

Fetal movements (FM) are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body ‘startle’ movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.

Lab-on-Skin: A Review of Flexible and Stretchable Electronics for Wearable Health Monitoring

Skin is the largest organ of the human body, and it offers a diagnostic interface rich with vital biological signals from the inner organs, blood vessels, muscles, and dermis/epidermis. Soft, flexible, and stretchable electronic devices provide a novel platform to interface with soft tissues for robotic feedback and control, regenerative medicine, and continuous health monitoring. Here, we introduce the term "lab-on-skin" to describe a set of electronic devices that have physical properties, such as thickness, thermal mass, elastic modulus, and water-vapor permeability, which resemble those of the skin. These devices can conformally laminate on the epidermis to mitigate motion artifacts and mismatches in mechanical properties created by conventional, rigid electronics while simultaneously providing accurate, non-invasive, long-term, and continuous health monitoring. Recent progress in the design and fabrication of soft sensors with more advanced capabilities and enhanced reliability suggest an impending translation of these devices from the research lab to clinical environments. Regarding these advances, the first part of this manuscript reviews materials, design strategies, and powering systems used in soft electronics. Next, the paper provides an overview of applications of these devices in cardiology, dermatology, electrophysiology, and sweat diagnostics, with an emphasis on how these systems may replace conventional clinical tools. The review concludes with an outlook on current challenges and opportunities for future research directions in wearable health monitoring.

Consumers' perceived attitudes to wearable devices in health monitoring in China: A survey study

BACKGROUND AND OBJECTIVE

Capital market and consumer market interest in wearable devices has surged in recent years, however, their actual acceptance in the field of health monitoring is somewhat not as expected. This study aims to understand the perceptions of wearable devices of general consumers, analyze the review of the devices by users, and find existing problems associated with current wearable devices.

METHODS

Sojump.com, an on-line questionnaire tool, was used to generate the questionnaire, which focused on four aspects. The snowball sampling method was employed to collect questionnaires by making use of the author's social network.

RESULTS

(1) A total of 2058 valid questionnaires were received from the respondents from every province in China; of the respondents, 52.4% have used a wearable device. (2) The respondents had a low level of knowledge about wearable devices (2.79/5) but were optimistic with regard to the devices' future (3.86/5), and 84% recognized an acceptable price of less than 2000 RMB. Nearly half of the respondents were unwilling to continuously wear the device (47.1%) and share their health data (44.7%). (3) The functions of wearable devices that the respondents expected were mainly health management (63.5%), mobile phone accessories (61.9%), and location tracking (61.2%), and the promising hot future functions were mainly data analysis (74.2%), exercise coaching (60.5%), and child tracking (58.8%). Regarding the health monitoring functions, the respondents were most interested in heart health monitoring. (4) The respondents had different levels of emphasis regarding the existing problems of wearable devices at different use stages. Being easily damaged or lost (49.7%), being incapable of providing health recommendations based on data analysis (46.7%), and being uncomfortable to wear (45.8%) likely lead consumers to abandon the use of wearable devices.

CONCLUSIONS

Consumers are optimistic about the prospects of wearable devices; however, there is a large gap between the reliability of the measurement data, the ease of use, and the interpretation of measurement data of current wearable products and consumer expectations. Consumer demand for health management functions is higher than that for daily auxiliary-type functions, which is an issue that should be properly addressed and resolved by manufacturers.

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors

Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the need for physical confinement. In this study, we describe a physiological signal monitoring bed based on load cells and assess an algorithm to extract the heart rate and breathing rate from the measured load-cell signals. Four infants participated in a total of 13 experiments. As a reference signal, electrocardiogram and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiration information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiration information were found to have average performance errors of 2.55% and 2.66%, respectively. The experimental results verify the positive feasibility of BCG-based measurements in infants.

Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis

Since heart rate variability (HRV) analysis is widely used to evaluate the physiological status of the human body, devices specifically designed for such applications are needed. To this end, we developed a smart electrocardiography (ECG) patch. The smart patch measures ECG using three electrodes integrated into the patch, filters the measured signals to minimize noise, performs analog-to-digital conversion, and detects R-peaks. The measured raw ECG data and the interval between the detected R-peaks can be recorded to enable long-term HRV analysis. Experiments were performed to evaluate the performance of the built-in R-wave detection, robustness of the device under motion, and applicability to the evaluation of mental stress. The R-peak detection results obtained with the device exhibited a sensitivity of 99.29%, a positive predictive value of 100.00%, and an error of 0.71%. The device also exhibited less motional noise than conventional ECG recording, being stable up to a walking speed of 5 km/h. When applied to mental stress analysis, the device evaluated the variation in HRV parameters in the same way as a normal ECG, with very little difference. This device can help users better understand their state of health and provide physicians with more reliable data for objective diagnosis.

Measurement method of magnetic field for the wire suspended micro-pendulum accelerometer

Force producer is one of the core components of a Wire Suspended Micro-Pendulum Accelerometer; and the stability of permanent magnet in the force producer determines the consistency of the acceleration sensor’s scale factor. For an assembled accelerometer; direct measurement of magnetic field strength is not a feasible option; as the magnetometer probe cannot be laid inside the micro-space of the sensor. This paper proposed an indirect measurement method of the remnant magnetization of Micro-Pendulum Accelerometer. The measurement is based on the working principle of the accelerometer; using the current output at several different scenarios to resolve the remnant magnetization of the permanent magnet. Iterative Least Squares algorithm was used for the adjustment of the data due to nonlinearity of this problem. The calculated remnant magnetization was 1.035 T. Compared to the true value; the error was less than 0.001 T. The proposed method provides an effective theoretical guidance for measuring the magnetic field of the Wire Suspended Micro-Pendulum Accelerometer; correcting the scale factor and temperature influence coefficients; etc.