Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units

Pediatric Monitoring Technologies and Congenital Heart Disease: A Systematic Review

Outpatient monitoring of infants with congenital heart disease has been shown to significantly reduce rates of mortality in the single ventricle population. Despite the accelerating development of miniaturized biosensors and electronics, and a growing market demand for at-home monitoring devices, the application of these technologies to infants and children is significantly delayed compared with the development of devices for adults. This article aims to review the current landscape of available monitoring technologies and devices for pediatric patients to describe the gap between technologies and clinical needs with the goal of progressing development of clinically and scientifically validated pediatric monitoring devices.

Electronic Skin for Health Monitoring Systems: Properties, Functions, and Applications

Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field are discussed. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems.

Flexible Meso Electronics and Photonics Based on Cocoon Silk and Applications

Flexible electronics, applicable to enlarged health, AI big data medications, etc., have been one of the most important technologies of this century. Due to its particular mechanical properties, biocompatibility, and biodegradability, cocoon silk (or SF, silk fibroin) plays a key role in flexible electronics/photonics. The review begins with an examination of the hierarchical meso network structures of SF materials and introduces the concepts of meso reconstruction, meso doping, and meso hybridization based on the correlation between the structure and performance of silk materials. The SF meso functionalization was developed according to intermolecular nuclear templating. By implementation of the techniques of meso reconstruction and functionalization in the refolding of SF materials, extraordinary performance can be achieved. Relying on this strategy, particularly designed flexible electronic and photonic components can be developed. This review covers the latest ideas and technologies of meso flexible electronics and photonics based on SF materials/meso functionalization. As silk materials are biocompatible and human skin-friendly, SF meso flexible electronic/photonic components can be applied to wearable or implanted devices. These devices are applicable in human physiological signals and activities sensing/monitoring. In the case of human-machine interaction, the devices can be applicable in in-body information transmission, computation, and storage, with the potential for the combination of artificial intelligence and human intelligence.

Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates

Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.

Biocompatible Electronic Skins for Cardiovascular Health Monitoring

Cardiovascular diseases represent a significant threat to the overall well-being of the global population. Continuous monitoring of vital signs related to cardiovascular health is essential for improving daily health management. Currently, there has been remarkable proliferation of technology focused on collecting data related to cardiovascular diseases through daily electronic skin monitoring. However, concerns have arisen regarding potential skin irritation and inflammation due to the necessity for prolonged wear of wearable devices. To ensure comfortable and uninterrupted cardiovascular health monitoring, the concept of biocompatible electronic skin has gained substantial attention. In this review, biocompatible electronic skins for cardiovascular health monitoring are comprehensively summarized and discussed. The recent achievements of biocompatible electronic skin in cardiovascular health monitoring are introduced. Their working principles, fabrication processes, and performances in sensing technologies, materials, and integration systems are highlighted, and comparisons are made with other electronic skins used for cardiovascular monitoring. In addition, the significance of integrating sensing systems and the updating wireless communication for the development of the smart medical field is explored. Finally, the opportunities and challenges for wearable electronic skin are also examined.

In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens

Tears have emerged as a promising alternative to blood for diagnosing diabetes. Despite increasing attempts to measure tear glucose using smart contact lenses, the controversy surrounding the correlation between tear glucose and blood glucose still limits the clinical usage of tears. Herein, we present an in-depth investigation of the correlation between tear glucose and blood glucose using a wireless and soft smart contact lens for continuous monitoring of tear glucose. This smart contact lens is capable of quantitatively monitoring the tear glucose levels in basal tears excluding the effect of reflex tears which might weaken the relationship with blood glucose. Furthermore, this smart contact lens can provide an unprecedented level of continuous tear glucose data acquisition at sub-minute intervals. These advantages allow the precise estimation of lag time, enabling the establishment of the concept called 'personalized lag time'. This demonstration considers individual differences and is successfully applied to both non-diabetic and diabetic humans, as well as in animal models, resulting in a high correlation.

A three-dimensional liquid diode for soft, integrated permeable electronics

Wearable electronics with great breathability enable a comfortable wearing experience and facilitate continuous biosignal monitoring over extended periods1-3. However, current research on permeable electronics is predominantly at the stage of electrode and substrate development, which is far behind practical applications with comprehensive integration with diverse electronic components (for example, circuitry, electronics, encapsulation)4-8. Achieving permeability and multifunctionality in a singular, integrated wearable electronic system remains a formidable challenge. Here we present a general strategy for integrated moisture-permeable wearable electronics based on three-dimensional liquid diode (3D LD) configurations. By constructing spatially heterogeneous wettability, the 3D LD unidirectionally self-pumps the sweat from the skin to the outlet at a maximum flow rate of 11.6 ml cm-2 min-1, 4,000 times greater than the physiological sweat rate during exercise, presenting exceptional skin-friendliness, user comfort and stable signal-reading behaviour even under sweating conditions. A detachable design incorporating a replaceable vapour/sweat-discharging substrate enables the reuse of soft circuitry/electronics, increasing its sustainability and cost-effectiveness. We demonstrated this fundamental technology in both advanced skin-integrated electronics and textile-integrated electronics, highlighting its potential for scalable, user-friendly wearable devices.

Wireless Battery-free and Fully Implantable Organ Interfaces

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

Weaning from mechanical ventilation and assessment of extubation readiness

Tremendous advancements in neonatal respiratory care have contributed to the improved survival of extremely preterm infants (gestational age ≤ 28 weeks). While mechanical ventilation is often considered one of the most important breakthroughs in neonatology, it is also associated with numerous short and long-term complications. For those reasons, clinical research has focused on strategies to avoid or reduce exposure to mechanical ventilation. Nonetheless, in the extreme preterm population, 70-100% of infants born 22-28 weeks of gestation are exposed to mechanical ventilation, with nearly 50% being ventilated for ≥ 3 weeks. As contemporary practices have shifted towards selectively reserving mechanical ventilation for those patients, mechanical ventilation weaning and extubation remain a priority yet offer a heightened challenge for clinicians. In this review, we will summarize the evidence for different strategies to expedite weaning and assess extubation readiness in preterm infants, with a particular focus on extremely preterm infants.

Porous Conductive Textiles for Wearable Electronics

Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.

From lab to life: how wearable devices can improve health equity

Wearable devices can provide personalised medicine at the point of need, potentially increasing access to health services and therefore improving health equity. Here the authors discuss their experiences developing wearable devices for vulnerable patient populations, including neonates and pregnant individuals.

Adaptive convolutional dictionary learning for denoising seismocardiogram to enhance the classification performance of aortic stenosis

BACKGROUND

Aortic stenosis (AS) is the most prevalent type of valvular heart disease (VHD), traditionally diagnosed using echocardiogram or phonocardiogram. Seismocardiogram (SCG), an emerging wearable cardiac monitoring modality, is proved to be feasible in non-invasive and cost-effective AS diagnosis. However, SCG waveforms acquired from patients with heart diseases are typically weak, making them more susceptible to noise contamination. While most related researches focus on motion artifacts, sensor noise and quantization noise have been mostly overlooked. These noises pose additional challenges for extracting features from the SCG, especially impeding accurate AS classification.

METHOD

To address this challenge, we present a convolutional dictionary learning-based method. Based on sparse modeling of SCG, the proposed method generates a personalized adaptive-size dictionary from noisy measurements. The dictionary is used for sparse coding of the noisy SCG into a transform domain. Reconstruction from the domain removes the noise while preserving the individual waveform pattern of SCG.

RESULTS

Using two self-collected SCG datasets, we established optimal dictionary learning parameters and validated the denoising performance. Subsequently, the proposed method denoised SCG from 50 subjects (25 AS and 25 non-AS). Leave-one-subject-out cross-validation (LOOCV) was applied to 5 machine learning classifiers. Among the classifiers, a bi-layer neural network achieved a moderate accuracy of 90.2%, with an improvement of 13.8% from the denoising.

CONCLUSIONS

The proposed sparsity-based denoising technique effectively removes stochastic sensor noise and quantization noise from SCG, consequently improving AS classification performance. This approach shows promise for overcoming instrumentation constraints of SCG-based diagnosis.

Challenges and prospects of visual contactless physiological monitoring in clinical study

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review

Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.

Versatile On-Chip Programming of Circuit Hardware for Wearable and Implantable Biomedical Microdevices

Wearable and implantable microscale electronic sensors have been developed for a range of biomedical applications. The sensors, typically millimeter size silicon microchips, are sought for multiple sensing functions but are severely constrained by size and power. To address these challenges, a hardware programmable application-specific integrated circuit design is proposed and post-process methodology is exemplified by the design of battery-less wireless microchips. Specifically, both mixed-signal and radio frequency circuits are designed by incorporating metal fuses and anti-fuses on the top metal layer to enable programmability of any number of features in hardware of the system-on-chip (SoC) designs. This is accomplished in post-foundry editing by combining laser ablation and focused ion beam processing. The programmability provided by the technique can significantly accelerate the SoC chip development process by enabling the exploration of multiple internal circuit parameters without the requirement of additional programming pads or extra power consumption. As examples, experimental results are described for sub-millimeter size complementary metal-oxide-semiconductor microchips being developed for wireless electroencephalogram sensors and as implantable microstimulators for neural interfaces. The editing technique can be broadly applicable for miniaturized biomedical wearables and implants, opening up new possibilities for their expedited development and adoption in the field of smart healthcare.

Near-Infrared Organic Photodetectors toward Skin-Integrated Photoplethysmography-Electrocardiography Multimodal Sensing System

In the fast-evolving landscape of decentralized and personalized healthcare, the need for multimodal biosensing systems that integrate seamlessly with the human body is growing rapidly. This presents a significant challenge in devising ultraflexible configurations that can accommodate multiple sensors and designing high-performance sensing components that remain stable over long periods. To overcome these challenges, ultraflexible organic photodetectors (OPDs) that exhibit exceptional performance under near-infrared illumination while maintaining long-term stability are developed. These ultraflexible OPDs demonstrate a photoresponsivity of 0.53 A W-1 under 940 nm, shot-noise-limited specific detectivity of 3.4 × 1013 Jones, and cut-off response frequency beyond 1 MHz at -3 dB. As a result, the flexible photoplethysmography sensor boasts a high signal-to-noise ratio and stable peak-to-peak amplitude under hypoxic and hypoperfusion conditions, outperforming commercial finger pulse oximeters. This ensures precise extraction of blood oxygen saturation in dynamic working conditions. Ultraflexible OPDs are further integrated with conductive polymer electrodes on an ultrathin hydrogel substrate, allowing for direct interface with soft and dynamic skin. This skin-integrated sensing platform provides accurate measurement of photoelectric and biopotential signals in a time-synchronized manner, reproducing the functionality of conventional technologies without their inherent limitations.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

Flexible and Scalable Dry Conductive Elastomeric Nanocomposites for Surface Stimulation Applications

OBJECTIVE

The study describes the development and testing of a dry surface stimulation flexible electrode (herein referred to as Flexatrode), a low-cost, flexible, and scalable elastomeric nanocomposite using carbon black (CB) and polydimethylsiloxane (PDMS).

METHODS

Flexatrodes composed of CB and PDMS were developed and tested for mechanical and functional stability up to 7 days. Uniform CB distribution was achieved by optimizing the dispersion process using toluene and methyl-terminated PDMS. Electromechanical testing in the through thickness directions over a long-term duration demonstrated stability of Flexatrode. Thermal stability of Flexatrode for up to a week was tested and validated, thus mitigating concerns of heat generation and deleterious skin reactions such as vasodilation or erythema.

RESULTS

25 wt.% CB was determined to be the optimal concentration. Electrical and thermal stability were demonstrated in the through thickness direction.

CONCLUSION

Flexatrode provides stable electrical properties combined with high flexibility and elasticity. Electrotherapy treated chronic wounds were 81.9% smaller than baseline at day 10. Wounds that received an inactive device (device without any electrical stimulation) were 58.1% smaller than baseline and wounds that received standard of care treatment were 62.2% smaller than baseline.

SIGNIFICANCE

The increasing need for wearable bioelectronics requiring long-term monitoring/treatment has highlighted the limitations of sustained use of gel-based electrodes. These can include skin irritation, bacterial overgrowth at the electrode site, gel dehydration over time, and signal degradation due to eccrine sweat formation. Flexatrode provides stable performance in a nanocomposite with scalable fabrication, thus providing a promising platform technology for wearable bioelectronics.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.

Self-Powered Sensing in Wearable Electronics─A Paradigm Shift Technology

With the advancements in materials science and micro/nanoengineering, the field of wearable electronics has experienced a rapid growth and significantly impacted and transformed various aspects of daily human life. These devices enable individuals to conveniently access health assessments without visiting hospitals and provide continuous, detailed monitoring to create comprehensive health data sets for physicians to analyze and diagnose. Nonetheless, several challenges continue to hinder the practical application of wearable electronics, such as skin compliance, biocompatibility, stability, and power supply. In this review, we address the power supply issue and examine recent innovative self-powered technologies for wearable electronics. Specifically, we explore self-powered sensors and self-powered systems, the two primary strategies employed in this field. The former emphasizes the integration of nanogenerator devices as sensing units, thereby reducing overall system power consumption, while the latter focuses on utilizing nanogenerator devices as power sources to drive the entire sensing system. Finally, we present the future challenges and perspectives for self-powered wearable electronics.

Clinical Evaluation of a Wireless Device for Monitoring Vitals in Newborn Babies

OBJECTIVES

To evaluate the ability of the Nemocare Raksha (NR), an internet of things (IoT)-enabled device, to continuously monitor vitals for 6 h and its safety in newborns. The accuracy of the device was also compared with the readings from the standard device used in the pediatric ward.

METHOD

Forty neonates (either gender) weighing ≥ 1.5 kg were included in the study. Heart rate, respiratory rate, body temperature, and oxygen saturation was measured using the NR and compared with standard care devices. Safety was assessed by monitoring for skin changes and local rise in temperature. The neonatal infant pain scale (NIPS) was used to assess pain and discomfort.

RESULT

A total of 227 h of observations (5.67 h per baby) were obtained. No discomfort or device-related adverse events were noted during the study period. The mean difference between the NR and the standard monitoring was 0.66 (0.42 to 0.90) for temperature (°C); -6.57 (-8.66 to -4.47) for heart rate (bpm); 7.60 (6.52 to 8.68) for respiratory rate (breaths per minute); -0.79 (-1.10 to -0.48) for oxygen saturation (%). The level of agreement analyzed using the intraclass correlation coefficient (ICC) was good for heart rate [ICC 0.77 (0.72 to 0.82); p value < 0.001] and oxygen saturation [ICC 0.80 (0.75 to 0.84); p value < 0.001]; moderate for body temperature [ICC 0.54 (0.36 to 0.60); p value < 0.001] and poor for respiratory rate [ICC 0.30 (0.10 to 0.44); p value 0.002].

CONCLUSION

The NR was able to seamlessly monitor vital parameters in neonates without any safety concern. The device showed a good level of agreement for heart rate and oxygen saturation among the four parameters measured.

Simple, miniaturized biosensors for wireless mapping of thermoregulatory responses

Temperature is the most commonly collected vital sign in all of clinical medicine; it plays a critical role in care decisions related to topics ranging from infection to inflammation, sleep, and fertility. Most assessments of body temperature occur at isolated anatomical locations (e.g. axilla, rectum, temporal artery, or oral cavity). Even this relatively primitive mode for monitoring can be challenging with vulnerable patient populations due to physical encumbrances and artifacts associated with the sizes, weights, shapes and mechanical properties of the sensors and, for continuous monitoring, their hard-wired interfaces to data collection units. Here, we introduce a simple, miniaturized, lightweight sensor as a wireless alternative, designed to address demanding applications such as those related to the care of neonates in high ambient humidity environments with radiant heating found in incubators in intensive care units. Such devices can be deployed onto specific anatomical locations of premature infants for homeostatic assessments. The estimated core body temperature aligns, to within 0.05 °C, with clinical grade, wired sensors, consistent with regulatory medical device requirements. Time-synchronized, multi-device operation across multiple body locations supports continuous, full-body measurements of spatio-temporal variations in temperature and additional modes of determining tissue health status in the context of sepsis detection and various environmental exposures. In addition to thermal sensing, these same devices support measurements of a range of other essential vital signs derived from thermo-mechanical coupling to the skin, for applications ranging from neonatal and infant care to sleep medicine and even pulmonary medicine.

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

Fabrication and characterization of polymer optical waveguide Bragg grating for pulse signal sensing

Polymer materials have the advantages of a low Young's modulus and low-cost preparation process. In this paper, a polymer-based optical waveguide pressure sensor based on a Bragg structure is proposed. The change in the Bragg wavelength in the output spectrum of the waveguide Bragg grating (WBG) is used to linearly characterize the change in pressure acting on the device. The polymer-based WBG was developed through a polymer film preparation process, and the experimental results show that the output signal of the device has a sensitivity of 1.275 nm/kPa with a measurement range of 0-12 kPa and an accuracy of 1 kPa. The experimental results indicate that the device already perfectly responds to a pulse signal. It has significant potential application value in medical diagnostics and health testing, such as blood pressure monitoring, sleep quality monitoring, and tactile sensing.

Microring structure for flexible polymer waveguide-based optical pressure sensing

Flexible pressure sensors provide a promising platform for artificial smart skins, and photonic devices provide a new technique to fabricate pressure sensors. Here, we present a flexible waveguide-based optical pressure sensor based on a microring structure. The waveguide-based optical pressure sensor is based on a five-cascade microring array structure with a size of 1500 µm × 500 µm and uses the change in output power to linearly characterize the change in pressure acting on the device. The results show that the device has a sensing range of 0-60 kPa with a sensitivity of 23.14 µW/kPa, as well as the ability to detect pulse signals, swallowing, hand gestures, etc. The waveguide-based pressure sensors offer the advantages of good output linearity, high integration density and easy-to-build arrays.

Communication Protocols Integrating Wearables, Ingestibles, and Implantables for Closed-Loop Therapies

Body-conformal sensors and tissue interfacing robotic therapeutics enable the real-time monitoring and treatment of diabetes, wound healing, and other critical conditions. By integrating sensors and drug delivery devices, scientists and engineers have developed closed-loop drug delivery systems with on-demand therapeutic capabilities to provide just-in-time treatments that correspond to chemical, electrical, and physical signals of a target morbidity. To enable closed-loop functionality in vivo, engineers utilize various low-power means of communication that reduce the size of implants by orders of magnitude, increase device lifetime from hours to months, and ensure the secure high-speed transfer of data. In this review, we highlight how communication protocols used to integrate sensors and drug delivery devices, such as radio frequency communication (e.g., Bluetooth, near-field communication), in-body communication, and ultrasound, enable improved treatment outcomes.

Boosting the Performance of Organic Photodetectors with a Solution-Processed Integration Circuit toward Ubiquitous Health Monitoring

Organic photodetectors, as an emerging wearable photoplethysmographic (PPG) technology, offer exciting opportunities for next-generation photonic healthcare electronics. However, the mutual restraints among photoresponse, structure complexity, and fabrication cost have intrinsically limited the development of organic photodetectors for ubiquitous health monitoring in daily activities. Here, an effective route to dramatically boost the performance of organic photodetectors with a solution-processed integration circuit for health monitoring application is reported. Through creating an ideal metal-semiconductor junction interface that minimizes the trap states within the device, solution-printed organic field-effect transistors (OFETs) are achieved with an ultrahigh signal amplification efficiency of 37.1 S A-1 , approaching the theoretical thermionic limit. Consequently, monolithic integration of the OFET with an organic photoconductor enables the remarkable amplification of photoresponse signal-to-noise ratio by more than four orders of magnitude from 5.5 to 4.6 × 105 , which is able to meet the demand for accurately extracting physiological information from the PPG waveforms. This work offers an effective and versatile approach to greatly enhance the photodetector performance, promising to revolutionize health monitoring technologies.

Infant movement classification through pressure distribution analysis

BACKGROUND

Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we propose an innovative non-intrusive approach using a pressure sensing device to classify infant general movements. Here we differentiate typical general movement patterns of the "fidgety period" (fidgety movements) vs. the "pre-fidgety period" (writhing movements).

METHODS

Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4 to 16 weeks of post-term age. 1776 pressure data snippets, each 5 s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present or absent. Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks.

RESULTS

Here we show that the convolution neural network achieved the highest average classification accuracy (81.4%). By comparing the pros and cons of other methods aiming at automated general movement assessment to the pressure sensing approach, we infer that the proposed approach has a high potential for clinical applications.

CONCLUSIONS

We conclude that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.

Machine learning-assisted flexible wearable device for tyrosine detection

Early diagnosis of pathological markers can significantly shorten the rate of viral transmission, reduce the probability of infection, and improve the cure rate of diseases. Therefore, analytical techniques for identifying pathological markers and environmental toxicants have received considerable attention from researchers worldwide. However, the most popular techniques used in clinical settings involve expensive precision instruments and complex detection processes. Thus, a simpler, more efficient, rapid, and intelligent means of analysis must be urgently developed. Electrochemical biosensors have the advantages of simple processing, low cost, low sample preparation requirements, rapid analysis, easy miniaturization, and integration. Thus, they have become popular in extensive research. Machine learning is widely used in material-assisted synthesis, sensor design, and other fields owing to its powerful data analysis and simulation learning capabilities. In this study, a machine learning-assisted carbon black-graphene oxide conjugate polymer (CB-GO/CP) electrode, in conjunction with a flexible wearable device, is proposed for the smart portable detection of tyrosine (Tyr). Input feature value data are obtained for the artificial neural network (ANN) and support vector machines (SVM) model learning via multiple data collections in artificial urine and by recording the pH and temperature values. The results reveal that a machine-learning model that integrates multiple external factors is more accurate for the prediction of Tyr concentration.

Replacing Monitoring Electrodes on Infant Skin Every 12 Versus 24 Hours

OBJECTIVE

To examine the effect of varying the frequency of monitoring electrode replacement on skin moisture and condition of infants hospitalized in the pediatric ICU.

METHODS

The population of the study consisted of 1- to 12-month-old infants receiving treatment in the pediatric ICU. The control group of the study (n = 33) included infants whose monitoring electrodes were replaced every 24 hours during monitoring, and the experimental group (n = 33) included infants whose monitoring electrodes were replaced every 12 and 24 hours during monitoring. Before assessment, the skin moisture of the monitoring areas was measured and evaluated with the Skin Condition Assessment Scale.

RESULTS

When the difference in skin moisture was compared for all measurement areas of the infants before monitoring and at the 24-hour mark, an increase in moisture was seen in both groups, and the difference in the experimental group was greater than that in the control group. Increased moisture is a risk factor for medical device-related pressure injuries. When comparing between-group differences in skin condition, the researchers noted a greater increase in skin condition score in the experimental group. An increased score indicates that the infant's skin condition is worsening.

CONCLUSIONS

Replacing the monitoring electrodes every 24 hours positively affected skin moisture and condition, whereas replacing them every 12 hours negatively affected skin moisture and condition.

Advanced wireless monitoring for children in the cardiac perioperative setting

INTRODUCTION

More than 40,000 children undergo surgical interventions annually for the treatment of congenital heart defects. Intraoperative and postoperative vital sign monitoring is a cornerstone of pediatric care.

METHODS

A single-arm prospective observational study was performed. Pediatric patients undergoing a procedure with a planned admission to the Cardiac Intensive Care Unit at Lurie Children's Hospital (Chicago, IL) were eligible for enrollment. Participant vital signs were monitored using standard equipment and an FDA-cleared experimental device (ANNE® ) consisting of a wireless patch positioned at the suprasternal notch and index finger or foot. The primary goal of the study was to assess real-world feasibility of wireless sensors in pediatric patients with congenital cardiac defects.

RESULTS

A total of 13 patients were enrolled, ranging in age from 4 months to 16 years with a median age of 4 years. Overall, 54% (n = 7) were female and the most common anomaly in the cohort was an atrial septal defect (n = 6). The mean admission length was 3 days (range 2-6), resulting in more than 1000 h of vital sign monitoring (⟩60,000 data points). Bland-Altman plots were generated for heart rate and respiratory rate to assess beat-to-beast differences between the standard equipment and the experimental sensors.

CONCLUSIONS

Novel, wireless, flexible sensors demonstrated comparable performance to standard monitoring equipment in a cohort of pediatric patients with congenital cardiac heart defects undergoing surgery.

Continuous sensing and quantification of body motion in infants: A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.

A comprehensive wireless neurological and cardiopulmonary monitoring platform for pediatrics

Neurodevelopment in the first 10 years of life is a critical time window during which milestones that define an individual's functional potential are achieved. Comprehensive multimodal neurodevelopmental monitoring is particularly crucial for socioeconomically disadvantaged, marginalized, historically underserved and underrepresented communities as well as medically underserved areas. Solutions designed for use outside the traditional clinical environment represent an opportunity for addressing such health inequalities. In this work, we present an experimental platform, ANNE EEG, which adds 16-channel cerebral activity monitoring to the existing, USA FDA-cleared ANNE wireless monitoring platform which provides continuous electrocardiography, respiratory rate, pulse oximetry, motion, and temperature measurements. The system features low-cost consumables, real-time control and streaming with widely available mobile devices, and fully wearable operation to allow a child to remain in their naturalistic environment. This multi-center pilot study successfully collected ANNE EEG recordings from 91 neonatal and pediatric patients at academic quaternary pediatric care centers and in LMIC settings. We demonstrate the practicality and feasibility to conduct electroencephalography studies with high levels of accuracy, validated via both quantitative and qualitative metrics, compared against gold standard systems. An overwhelming majority of parents surveyed during studies indicated not only an overall preference for the wireless system, but also that its use would improve their children's physical and emotional health. Our findings demonstrate the potential for the ANNE system to perform multimodal monitoring to screen for a variety of neurologic diseases that have the potential to negatively impact neurodevelopment.

Comparison of Wired and Wireless Heart Rate Monitoring in the Neonatal Intensive Care Unit

In the Neonatal Intensive Care Unit (NICU), infants' vital signs are monitored on a continuous basis via wired devices. These often interfere with patient care and pose increased risks of skin damage, infection, and tangling around the body. Recently, a wireless system for neonatal monitoring called ANNEⓇ One (Sibel Health, Chicago, USA) was developed. We designed an ongoing study to evaluate the feasibility, reliability and accuracy, of using this system in the NICU. Vital signals were simultaneously acquired by using the standard, wired clinical monitor and the ANNEⓇ device. Data from 10 NICU infants were recorded for 8 hours per day during 4 consecutive days. Initial analysis of the heart rate (HR) data revealed four problems in comparing the signals: 1) gaps in the signals - periods of time for which data were unavailable, 2) wired and wireless signals were sampled at different rates, 3) a delay between the sampled values of wired and wireless signals, and 4) this delay increased with time. To address these problems, we developed a pre-processing algorithm that interpolated samples in short gaps, resampled the signals to an equal rate, estimated the delay and drift rate between corresponding signals, and aligned the signals. Applications of the pre-processing algorithm to 40 recordings demonstrated that it was very effective. A strong agreement between wireless and wired HR signals was seen, with an average correlation of 0.95±0.04, a slope of 1.00, and a variance accounted for 89.56±7.62%. Bland-Altman analysis showed a low bias across the ensemble, with an average difference of 0.11 (95% confidence interval of -0.02 to 0.24) bpm.Clinical relevance- This algorithm provides the means for a detailed comparison of wired and wireless monitors in the NICU.

Development of an Interactive Dashboard to Analyse Physiological Signals in the Neonatal Intensive Care Unit

Premature babies and those born with a medical condition are cared for within the neonatal intensive care unit (NICU) in hospitals. Monitoring physiological signals and subsequent analysis and interpretation can reveal acute and chronic conditions for these neonates. Several advanced algorithms using physiological signals have been built into existing monitoring systems to allow clinicians to analyse signals in real time and anticipate patient deterioration. However, limited enhancements have been made to interactively visualise and adapt them to neonatal monitoring systems. To bridge this gap, we describe the development of a user-friendly and interactive dashboard for neonatal vital signs analysis written in the Python programming language where the analysis can be performed without prior computing knowledge. To ensure practicality, the dashboard was designed in consultation with a neonatologist to visualise electrocardiogram, heart rate, respiratory rate and oxygen saturation data in a time-series format. The resulting dashboard included interactive visualisations, advanced electrocardiogram analysis and statistical analysis which can be used to extract important information on patients' conditions.Clinical Relevance- This will support the care of preterm infants by allowing clinicians to visualise and interpret physiological data in greater granularity, aiding in patient monitoring and detection of adverse conditions. The detection of adverse conditions could allow timely and potentially life-saving interventions for conditions such as sepsis and brain injury.

The use of wireless sensors in the neonatal intensive care unit: a study protocol

Background

Continuous monitoring of vital signs and other biological signals in the Neonatal Intensive Care Unit (NICU) requires sensors connected to the bedside monitors by wires and cables. This monitoring system presents challenges such as risks for skin damage or infection, possibility of tangling around the patient body, or damage of the wires, which may complicate routine care. Furthermore, the presence of cables and wires can act as a barrier for parent-infant interactions and skin to skin contact. This study will investigate the use of a new wireless sensor for routine vital monitoring in the NICU.

Methods

Forty-eight neonates will be recruited from the Montreal Children's Hospital NICU. The primary outcome is to evaluate the feasibility, safety, and accuracy of a wireless monitoring technology called ANNE^® One (Sibel Health, Niles, MI, USA). The study will be conducted in 2 phases where physiological signals will be acquired from the standard monitoring system and the new wireless monitoring system simultaneously. In phase 1, participants will be monitored for 8 h, on four consecutive days, and the following signals will be obtained: heart rate, respiratory rate, oxygen saturation and skin temperature. In phase 2, the same signals will be recorded, but for a period of 96 consecutive hours. Safety and feasibility of the wireless devices will be assessed. Analyses of device accuracy and performance will be accomplished offline by the biomedical engineering team.

Conclusion

This study will evaluate feasibility, safety, and accuracy of a new wireless monitoring technology in neonates treated in the NICU.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Recent Advancements in Physiological, Biochemical, and Multimodal Sensors Based on Flexible Substrates: Strategies, Technologies, and Integrations

Flexible wearable devices have been widely used in biomedical applications, the Internet of Things, and other fields, attracting the attention of many researchers. The physiological and biochemical information on the human body reflects various health states, providing essential data for human health examination and personalized medical treatment. Meanwhile, physiological and biochemical information reveals the moving state and position of the human body, and it is the data basis for realizing human-computer interactions. Flexible wearable physiological and biochemical sensors provide real-time, human-friendly monitoring because of their light weight, wearability, and high flexibility. This paper reviews the latest advancements, strategies, and technologies of flexibly wearable physiological and biochemical sensors (pressure, strain, humidity, saliva, sweat, and tears). Next, we systematically summarize the integration principles of flexible physiological and biochemical sensors with the current research progress. Finally, important directions and challenges of physiological, biochemical, and multimodal sensors are proposed to realize their potential applications for human movement, health monitoring, and personalized medicine.

Recent Advances in Bioinspired Hydrogels: Materials, Devices, and Biosignal Computing

The remarkable ability of biological systems to sense and adapt to complex environmental conditions has inspired new materials and novel designs for next-generation wearable devices. Hydrogels are being intensively investigated for their versatile functions in wearable devices due to their superior softness, biocompatibility, and rapid stimulus response. This review focuses on recent strategies for developing bioinspired hydrogel wearable devices that can accommodate mechanical strain and integrate seamlessly with biological systems. We will provide an overview of different types of bioinspired hydrogels tailored for wearable devices. Next, we will discuss the recent progress of bioinspired hydrogel wearable devices such as electronic skin and smart contact lenses. Also, we will comprehensively summarize biosignal readout methods for hydrogel wearable devices as well as advances in powering and wireless data transmission technologies. Finally, current challenges facing these wearable devices are discussed, and future directions are proposed.

A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments

STUDY OBJECTIVES

We assessed the real-world performance of the ANNE Sleep system against 2 Food and Drug Administration-cleared home sleep testing platforms and the intraindividual night-to-night variability of respiratory event index measured by ANNE Sleep.

METHODS

We evaluated the home performance of the ANNE Sleep system compared with 2 Food and Drug Administration-cleared home sleep testing platforms (WatchPAT: n = 29 and Alice NightOne: n = 46) during a synchronous night with unsupervised patient application. Additionally, we evaluated night-to-night variability of respiratory event index and total sleep time using the ANNE Sleep system (n = 30).

RESULTS

For the diagnosis of moderate and severe obstructive sleep apnea, the ANNE Sleep system had a positive percent agreement of 58% (95% confidence interval, 28-85%) and a negative percent agreement of 100% (95% confidence interval, 80-100%) compared to WatchPAT. The positive and negative percent agreement for ANNE Sleep vs Alice NightOne was 85% (95% confidence interval, 66-96%) and 95% (95% confidence interval, 74-100%). There were no differences in mean total sleep time or respiratory event index across multiple nights of monitoring with ANNE. There were no differences consistent with a first-night effect but testing multiple nights reclassified obstructive sleep apnea severity in 5 (17%) individuals and detected 3 additional cases of moderate disease, with only a 12% (standard deviation, 28%) mean fluctuation in respiratory event index from the first night of testing compared to a mean of multiple nights. Overall, 80% of users found ANNE comfortable and easy to use.

CONCLUSIONS

ANNE Sleep exhibited stronger concordance with Alice NightOne compared to WatchPAT. While we illustrated low night-to-night variability for ANNE Sleep, the results suggest multiple nights increased detection of moderate or severe obstructive sleep apnea.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: ANNE Diagnostic Agreement With Home Sleep Testing; URL: https://clinicaltrials.gov/ct2/show/NCT05421754; Identifier: NCT05421754.

CITATION

Walter J, Lee JY, Blake S, et al. A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments. J Clin Sleep Med. 2023;19(5):865-872.

Ultrathin, soft, radiative cooling interfaces for advanced thermal management in skin electronics

Thermal management plays a notable role in electronics, especially for the emerging wearable and skin electronics, as the level of integration, multifunction, and miniaturization of such electronics is determined by thermal management. Here, we report a generic thermal management strategy by using an ultrathin, soft, radiative-cooling interface (USRI), which allows cooling down the temperature in skin electronics through both radiative and nonradiative heat transfer, achieving temperature reduction greater than 56°C. The light and intrinsically flexible nature of the USRI enables its use as a conformable sealing layer and hence can be readily integrated with skin electronics. Demonstrations include passive cooling down of Joule heat for flexible circuits, improving working efficiency for epidermal electronics, and stabling performance outputs for skin-interfaced wireless photoplethysmography sensors. These results offer an alternative pathway toward achieving effective thermal management in advanced skin-interfaced electronics for multifunctionally and wirelessly operated health care monitoring.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Self-Powered Wireless Flexible Ionogel Wearable Devices

Ionogels are promising soft materials for flexible wearable devices because of their unique features such as ionic conductivity and thermal stability. Ionogels reported to date show excellent sensing sensitivity; however, they suffer from a complicated external power supply. Herein, we report a self-powered wearable device based on an ionogel incorporating poly(vinylidene fluoride) (PVDF). The three-dimensional (3D) printed PVDF-ionogel exhibits amazing stretchability (1500%), high conductivity (0.36 S/m at 105 Hz), and an extremely low glass transition temperature (-84 °C). Moreover, the flexible wearable devices assembled from the PVDF-ionogel can precisely detect physiological signals (e.g., wrist, gesture, running, etc.) with a self-powered supply. Most significantly, a self-powered wireless flexible wearable device based on our PVDF-ionogel achieves monitoring healthcare of a human by transmitting obtained signals with a Bluetooth module timely and accurately. This work provides a facile and efficient method for fabricating cost-effective wireless wearable devices with a self-powered supply, enabling their potential applications for healthcare, motion detection, human-machine interfaces, etc.

Pathway of transient electronics towards connected biomedical applications

Transient electronic devices have shown promising applications in hardware security and medical implants with diagnosing therapeutics capabilities since their inception. Control of the device transience allows the device to "dissolve at will" after its functional operation, leading to the development of on-demand transient electronics. This review discusses the recent developments and advantages of triggering strategies (e.g., electrical, thermal, ultrasound, and optical) for controlling the degradation of on-demand transient electronics. We also summarize bioresorbable sensors for medical diagnoses, including representative applications in electrophysiology and neurochemical sensing. Along with the profound advancements in medical diagnosis, the commencement of therapeutic systems such as electrical stimulation and drug delivery for the biomedical or medical implant community has also been discussed. However, implementing a transient electronic system in real healthcare infrastructure is still in its infancy. Many critical challenges still need to be addressed, including strategies to decouple multimodal sensing signals, dissolution selectivity in the presence of multiple stimuli, and a complete sensing-stimulation closed-loop system. Therefore, the review discusses future opportunities in transient decoupling sensors and robust transient devices, which are selective to a particular stimulus and act as hardware-based passwords. Recent advancements in closed-loop controller-enabled electronics have also been analyzed for future opportunities of using data-driven artificial intelligence-powered controllers in fully closed-loop transient systems.