At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics

Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: persistent fatigue can induce cognitive stress, while chronic stress impairs sleep quality, creating a harmful feedback loop. Here, we introduce a wireless, soft, multifunctional bioelectronic system offering the continuous real-time detection and management of comprehensive mental states. The all-in-one wearable device, mounted on the forehead, measures clinical-grade brain and cardiorespiratory signals. This membrane biopatch is imperceptible, flexible, and reusable, providing ultimate user comfort while detecting high-fidelity electroencephalogram, electrooculogram, pulse rate, and blood oxygen saturation. A set of in vivo studies with human subjects demonstrates that the soft device has great skin-conformal contact and minimized motion artifacts, capturing clinical-quality data with different activities, even during sleep. The developed signal processing methods and deep-learning algorithms offer automated, real-time classification of driving drowsiness, stress conditions, and sleep quality. The bioelectronics platforms in this study have the potential to revolutionize digital healthcare, particularly personalized medicine and at-home health monitoring.

Ultrahigh Charge Density of Cellulose-Based Triboelectric Materials Based on Built-in Electric Field and Deep Trap Synergy

Cellulose-based triboelectric nanogenerators (TENGs) are increasingly studied as potential candidates for advancing sustainable wearable electronics due to their biodegradability, self-powering capability, and high sensitivity. However, the near-electroneutrality of cellulose and its lack of efficient charge storage sites result in rapid charge dissipation. This study's synergistic approach of constructing deep traps and built-in electric fields effectively promotes charge trapping. This approach achieved nearly 2 orders of magnitude improvement in the deep-trap density of the modified cellulose and a 74% reduction in the charge dissipation rate, compared with cellulose, yielding a charge density as high as 332 μC/m2, comparable to the output produced by the ion injection. The integrated TENG demonstrates reliable and high-sensitivity signal transmission as a wearable electronic device. This study presents a simple and scalable strategy for fabricating high-performance cellulose-based TENGs, underscoring the significant potential of cellulose in sustainable self-powered wearable electronics.

Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review

Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intelligence technologies has revolutionized the treatment and diagnosis of sleep apnea. Leveraging the portability and sensors of wearable devices, coupled with AI algorithms, has enabled real-time monitoring and accurate analysis of sleep patterns, facilitating early detection and personalized interventions for people suffering from sleep apnea. This article presents a systematic review of the current state of the art in identifying the latest artificial intelligence techniques, wearable devices, data types, and preprocessing methods employed in the diagnosis of sleep apnea. Four databases were used and the results before screening report 249 studies published between 2020 and 2024. After screening, 28 studies met the inclusion criteria. This review reveals a trend in recent years where methodologies involving patches, clocks and rings have been increasingly integrated with convolutional neural networks, producing promising results, particularly when combined with transfer learning techniques. We observed that the outcomes of various algorithms and their combinations also rely on the quantity and type of data utilized for training. The findings suggest that employing multiple combinations of different neural networks with convolutional layers contributes to the development of a more precise system for early diagnosis of sleep apnea.

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for diagnosing sleep disorders; however, its discomfort and inconvenience limit its accessibility. To address these issues, a wearable device (WD) integrated with stretchable transparent electrodes (STEs) is developed in this study for multisignal sleep monitoring and artificial intelligence (AI)-driven sleep staging. Utilizing conductive and flexible STEs, the WD records multiple biological signals (electroencephalogram [EEG], electrooculogram [EOG], electromyogram [EMG], photoplethysmography, and motion data) with high precision and low noise, comparable to PSG (<4 μVRMS). It achieves a 73.2% accuracy and a macro F1 score of 0.72 in sleep staging using an AI model trained on multisignal inputs. Notably, accuracy marginally improves when using only the EEG, EOG, and EMG channels, which may simplify future device designs. This WD offers a compact, multisignal solution for at-home sleep monitoring, with the potential for use as an evaluation tool for personalized sleep therapies.

Implications of heart rate variability measured using wearable electrocardiogram devices in diagnosing Parkinson's disease and its association with neuroimaging biomarkers: a case-control study

Introduction

Heart rate variability (HRV) reflects cardiac autonomic regulation, and reduced HRV is associated with Parkinson's disease (PD). However, studies regarding the implications of HRV measures for the clinical manifestations of PD have shown inconclusive results. We examined the relationship between HRV measures obtained via long-term monitoring using a wearable electrocardiogram (ECG) device and the diagnosis and clinical characteristics of PD.

Methods

Seventeen controls and 20 patients with PD were prospectively enrolled. The HRV measures were recorded using a wearable ECG device for up to 72 h. Time- and frequency-domain measures were derived from the HRV analysis, and their association with PD diagnosis and clinical features was investigated. We investigated their association with neuroimaging biomarkers using magnetic resonance imaging to explore the underlying neural correlates.

Results

The diagnosis of PD was associated with several HRV measures, including a decreased standard deviation of N-N intervals, standard deviation of all heart rates, and low-frequency (LF) power. Among these HRV measures, only LF power was associated with clinical features of PD. LF power was positively correlated with the tremor sub-score (r = 0.500; p = 0.035) and negatively associated with the left (r = -0.598; p = 0.024) and right (r = -0.693; p = 0.006) cerebellar hemispheres in patients with PD.

Conclusion

Low-frequency power may be used as a biomarker for tremor-associated pathophysiology of PD. Moreover, a wearable ECG device with its capability for long-term monitoring might be a promising tool for diagnosing PD.

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review

BACKGROUND

Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)-powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems.

OBJECTIVE

This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis.

METHODS

Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach.

RESULTS

The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%).

CONCLUSIONS

Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.

Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence

Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, and continuous health monitoring without disrupting daily life. This integration facilitates a shift from traditional, hospital-centered healthcare to a more decentralized, patient-centric model, where wearable sensors can collect real-time physiological data, provide deep analysis of these data streams, and generate actionable insights for point-of-care precise diagnostics and personalized therapy. Despite rapid advancements in smart materials, machine learning, and wearable sensing technologies, there is a lack of comprehensive reviews that systematically examine the intersection of these fields. This review addresses this gap, providing a critical analysis of wearable sensing technologies empowered by smart advanced materials and artificial Intelligence. The state-of-the-art smart materials-including self-healing, metamaterials, and responsive materials-that enhance sensor functionality are first examined. Advanced machine learning methodologies integrated into wearable devices are discussed, and their role in biomedical applications is highlighted. The combined impact of wearable sensors, empowered by smart materials and machine learning, and their applications in intelligent diagnostics and therapeutics are also examined. Finally, existing challenges, including technical and compliance issues, information security concerns, and regulatory considerations are addressed, and future directions for advancing intelligent healthcare are proposed.

Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces

Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm-2) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.

An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor

Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). Thirty-seven subjects underwent overnight monitoring using an HSAT device and the Soomirang system simultaneously. Normal and apnea events were classified using an MLP-Mixer deep learning model based on Soomirang data, which was also used to estimate total sleep time (ST). Pearson correlation and Bland-Altman analyses were conducted to evaluate the agreement of ST and the apnea-hypopnea index (AHI) calculated by the HSAT device and Soomirang. ST demonstrated a correlation of 0.9 with an average time difference of 7.5 min, while AHI showed a correlation of 0.95 with an average AHI difference of 3. The accuracy, sensitivity, and specificity of the Soomirang for detecting OSA were 97.14%, 100%, and 95.45% at AHI ≥ 15, respectively. The proposed algorithm, utilizing data from a wireless abdomen-worn device exhibited excellent performance in detecting moderate to severe OSA. The findings underscored the potential of a simple device as an accessible and effective tool for OSA screening and follow-up.

Event-Level Identification of Sleep Apnea Using FMCW Radar

Sleep apnea, characterized by its high prevalence and serious health consequences, faces a critical bottleneck in diagnosis. Polysomnography (PSG), the gold standard, is costly and cumbersome, while wearable devices struggle with quality control and patient compliance, rendering them as unsuitable for both large-scale screening and continuous monitoring. To address these challenges, this research introduces a contactless, low-cost, and accurate event-level sleep apnea detection method leveraging frequency-modulated continuous-wave (FMCW) radar technology. The core of our approach is a novel deep-learning model, built upon the U-Net architecture and augmented with self-attention mechanisms and squeeze-and-excitation (SE) modules, meticulously designed for the precise event-level segmentation of sleep apnea from FMCW radar signals. Crucially, we integrate blood oxygen saturation (SpO2) prediction as an auxiliary task within a multitask-learning framework to enhance the model's feature extraction capabilities and clinical utility by capturing physiological correlations between apnea events and oxygen levels. Rigorous evaluation in a clinical dataset, comprising data from 35 participants, with synchronized PSG and radar data demonstrated a performance exceeding that of the baseline methods (Base U-Net and CNN-MHA), achieving a high level of accuracy in event-level segmentation (with an F1-score of 0.8019) and OSA severity grading (91.43%). These findings underscore the significant potential of our radar-based event-level detection system as a non-contact, low-cost, and accurate solution for OSA assessment. This technology offers a promising avenue for transforming sleep apnea diagnosis, making large-scale screening and continuous home monitoring a practical reality and ultimately leading to improved patient outcomes and public health impacts.

Advances in Soft Strain and Pressure Sensors

Soft strain and pressure sensors represent a breakthrough in material engineering and nanotechnology, providing accurate and reliable signal detection for applications in health monitoring, sports management, human-machine interface, or soft robotics, when compared to traditional rigid sensors. However, their performance is often compromised by environmental interference and off-axis mechanical deformations, which lead to nonspecific responses, as well as unstable and inaccurate measurements. These challenges can be effectively addressed by enhancing the sensors' specificity, making them responsive only to the desired stimulus while remaining insensitive to unwanted stimuli. This review systematically examines various materials and design strategies for developing strain and pressure sensors with high specificity for target physical signals, such as tactility, pressure distribution, body motions, or artery pulse. This review highlights approaches in materials engineering that impart special properties to the sensors to suppress interference from factors such as temperature, humidity, and liquid contact. Additionally, it details structural designs that improve sensor performance under different types of off-axis mechanical deformations. This review concludes by discussing the ongoing challenges and opportunities for inspiring the future development of highly specific electromechanical sensors.

Bio-inspired electronics: Soft, biohybrid, and "living" neural interfaces

Neural interface technologies are increasingly evolving towards bio-inspired approaches to enhance integration and long-term functionality. Recent strategies merge soft materials with tissue engineering to realize biologically-active and/or cell-containing living layers at the tissue-device interface that enable seamless biointegration and novel cell-mediated therapeutic opportunities. This review maps the field of bio-inspired electronics and discusses key recent developments in tissue-like and regenerative bioelectronics, from soft biomaterials and surface-functionalized bioactive coatings to cell-containing 'biohybrid' and 'all-living' interfaces. We define and contextualize key terminology in this emerging field and highlight how biological and living components can bridge the gap to clinical translation.

A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1 to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artifacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation <10%. Coupled with deep learning, explainable AI, and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management.

Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders

This paper explores the progressive era of sleep monitoring, focusing on wearable and remote devices contributing to advances in the concept of home polysomnography. We begin by exploring the basic physiology of sleep, establishing a theoretical basis for understanding sleep stages and associated changes in physiological variables. The review then moves on to an analysis of specific cutting-edge devices and technologies, with an emphasis on their practical applications, user comfort, and accuracy. Attention is also given to the ability of these devices to predict neurological disorders, particularly Alzheimer's and Parkinson's disease. The paper highlights the integration of hardware innovations, targeted sleep parameters, and partially advanced algorithms, illustrating how these elements converge to provide reliable sleep health information. By bridging the gap between clinical diagnosis and real-world applicability, this review aims to elucidate the role of modern sleep monitoring tools in improving personalised healthcare and proactive disease management.

Soft Nanomembrane Sensor-Enabled Wearable Multimodal Sensing and Feedback System for Upper-Limb Sensory Impairment Assistance

Sensory rehabilitation in pediatric patients with traumatic spinal cord injury is challenging due to the ongoing development of their nervous systems. However, these sensory problems often result in nonuse of the impaired limb, which disturbs impaired limb rehabilitation and leads to overuse of the contralateral limb and other physical or psychological issues that may persist. Here, we introduce a soft nanomembrane sensor-enabled wearable glove system that wirelessly delivers a haptic sensation from the hand with tactile feedback responses for sensory impairment assistance. The smart glove system uses gold nanomembranes, copper-elastomer composites, and laser-induced graphene for the sensitive detection of pressure, temperature, and strain changes. The nanomaterial sensors are integrated with low-profile tactile actuators and wireless flexible electronics to offer real-time sensory feedback. The wearable system's thin-film sensors demonstrate 98% and 97% accuracy in detecting pressure and finger flexion, respectively, along with a detection coverage of real-life temperature changes as an effective rehabilitation tool. Collectively, the upper-limb sensory impairment assistance system embodies the latest in soft materials and wearable technology to incorporate soft sensors and miniaturized actuators and maximize its compatibility with human users, offering a promising solution for patient sensory rehabilitation.

Advances in Photonic Materials and Integrated Devices for Smart and Digital Healthcare: Bridging the Gap Between Materials and Systems

Recent advances in developing photonic technologies using various materials offer enhanced biosensing, therapeutic intervention, and non-invasive imaging in healthcare. Here, this article summarizes significant technological advancements in materials, photonic devices, and bio-interfaced systems, which demonstrate successful applications for impacting human healthcare via improved therapies, advanced diagnostics, and on-skin health monitoring. The details of required materials, necessary properties, and device configurations are described for next-generation healthcare systems, followed by an explanation of the working principles of light-based therapeutics and diagnostics. Next, this paper shares the recent examples of integrated photonic systems focusing on translation and immediate applications for clinical studies. In addition, the limitations of existing materials and devices and future directions for smart photonic systems are discussed. Collectively, this review article summarizes the recent focus and trends of technological advancements in developing new nanomaterials, light delivery methods, system designs, mechanical structures, material functionalization, and integrated photonic systems to advance human healthcare and digital healthcare.

Paintable, Fast Gelation, Highly Adhesive Hydrogels for High-fidelity Electrophysiological Monitoring Wirelessly

High-fidelity wireless electrophysiological monitoring is essential for ambulatory healthcare applications. Soft solid-like hydrogels have received significant attention as epidermal electrodes because of their tissue-like mechanical properties and high biocompatibility. However, it is challenging to develop a hydrogel electrode that provides robust contact and high adhesiveness with glabrous skin and hairy scalp for high-fidelity, continuous electrophysiological signal detection. Here, a paintable, fast gelation, highly adhesive, and conductive hydrogel is engineered for high-fidelity wireless electrophysiological monitoring. The hydrogel, consisting of gelatin, gallic acid, sodium citrate, lithium chloride, glycerol, and Tris-HCl buffer solution exhibits a reversible thermal phase transition capability, which endows it with the attributes of on-skin applicability and fast in situ gelation with 15 s, thereby addressing the aforementioned limitations. The introduction of gallic acid enhances the adhesive properties of the hydrogel, facilitating secure electrode attachment to the skin or hairy scalp. To accentuate the potential applications in at-home electrophysiological health monitoring, the hydrogel electrodes are demonstrated for high-fidelity electrocardiogram recording for one hour during various daily activities, as well as in simultaneous electroencephalogram and electrocardiogram recording during a 30 min nap.

Highly Self-Adhesive and Biodegradable Silk Bioelectronics for All-In-One Imperceptible Long-Term Electrophysiological Biosignals Monitoring

Skin-like bioelectronics offer a transformative technological frontier, catering to continuous and real-time yet highly imperceptible and socially discreet digital healthcare. The key technological breakthrough enabling these innovations stems from advancements in novel material synthesis, with unparalleled possibilities such as conformability, miniature footprint, and elasticity. However, existing solutions still lack desirable properties like self-adhesivity, breathability, biodegradability, transparency, and fail to offer a streamlined and scalable fabrication process. By addressing these challenges, inkjet-patterned protein-based skin-like silk bioelectronics (Silk-BioE) are presented, that integrate all the desirable material features that have been individually present in existing devices but never combined into a single embodiment. The all-in-one solution possesses excellent self-adhesiveness (300 N m-1) without synthetic adhesives, high breathability (1263 g h-1 m-2) as well as swift biodegradability in soil within a mere 2 days. In addition, with an elastic modulus of ≈5 kPa and a stretchability surpassing 600%, the soft electronics seamlessly replicate the mechanics of epidermis and form a conformal skin/electrode interface even on hairy regions of the body under severe perspiration. Therefore, coupled with a flexible readout circuitry, Silk-BioE can non-invasively monitor biosignals (i.e., ECG, EEG, EOG) in real-time for up to 12 h with benchmarking results against Ag/AgCl electrodes.

ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging

By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, existing algorithms cannot adapt well to computing scenarios with limited computing power, such as portable sleep detection and consumer-level sleep disorder screening. In addition, existing algorithms still have the problem of N1 confusion. To address these issues, we propose an efficient sleep sequence network (ESSN) with an ingenious structure to achieve efficient automatic sleep staging at a low computational cost. A novel N1 structure loss is introduced based on the prior knowledge of N1 transition probability to alleviate the N1 stage confusion problem. On the SHHS dataset containing 5,793 subjects, the overall accuracy, macro F1, and Cohen's kappa of ESSN are 88.0%, 81.2%, and 0.831, respectively. When the input length is 200, the parameters and floating-point operations of ESSN are 0.27M and 0.35G, respectively. With a lead in accuracy, ESSN inference is twice as fast as L-SeqSleepNet on the same device. Therefore, our proposed model exhibits solid competitive advantages comparing to other state-of-the-art automatic sleep staging methods.

Beyond Flexible: Unveiling the Next Era of Flexible Electronic Systems

Flexible electronics are integral in numerous domains such as wearables, healthcare, physiological monitoring, human-machine interface, and environmental sensing, owing to their inherent flexibility, stretchability, lightweight construction, and low profile. These systems seamlessly conform to curvilinear surfaces, including skin, organs, plants, robots, and marine species, facilitating optimal contact. This capability enables flexible electronic systems to enhance or even supplant the utilization of cumbersome instrumentation across a broad range of monitoring and actuation tasks. Consequently, significant progress has been realized in the development of flexible electronic systems. This study begins by examining the key components of standalone flexible electronic systems-sensors, front-end circuitry, data management, power management and actuators. The next section explores different integration strategies for flexible electronic systems as well as their recent advancements. Flexible hybrid electronics, which is currently the most widely used strategy, is first reviewed to assess their characteristics and applications. Subsequently, transformational electronics, which achieves compact and high-density system integration by leveraging heterogeneous integration of bare-die components, is highlighted as the next era of flexible electronic systems. Finally, the study concludes by suggesting future research directions and outlining critical considerations and challenges for developing and miniaturizing fully integrated standalone flexible electronic systems.

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa-1 with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.

A selective frequency damping and Janus adhesive hydrogel as bioelectronic interfaces for clinical trials

Maintaining stillness is essential for accurate bioelectrical signal acquisition, but dynamic noise from breathing remains unavoidable. Isotropic adhesives are often used as bioelectronic interfaces to ensure signal fidelity, but they can leave irreversible residues, compromising device accuracy. We propose a hydrogel with selective frequency damping and asymmetric adhesion as a bioelectronic interface. This hydrogel mitigates dynamic noise from breathing, with a damping effect in the breathing frequency range 60 times greater than at other frequencies. It also exhibits an asymmetric adhesion difference of up to 537 times, preventing residues. By homogenizing ion distribution, extending Debye length, and densifying the electric field, the hydrogel ensures stable signal transmission over 10,000 cycles. Additionally, it can non-invasively diagnose otitis media with higher sensitivity than invasive probes and has been effective in clinical polysomnography monitoring, aiding in the diagnosis of obstructive sleep apnea.

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis

BACKGROUND

Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography.

OBJECTIVE

The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity.

METHODS

Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis.

RESULTS

Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices.

CONCLUSIONS

Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

All-in-One, Wireless, Multi-Sensor Integrated Athlete Health Monitor for Real-Time Continuous Detection of Dehydration and Physiological Stress

Athletes are at high risk of dehydration, fatigue, and cardiac disorders due to extreme performance in often harsh environments. Despite advancements in sports training protocols, there is an urgent need for a non-invasive system capable of comprehensive health monitoring. Although a few existing wearables measure athlete's performance, they are limited by a single function, rigidity, bulkiness, and required straps and adhesives. Here, an all-in-one, multi-sensor integrated wearable system utilizing a set of nanomembrane soft sensors and electronics, enabling wireless, real-time, continuous monitoring of saliva osmolality, skin temperature, and heart functions is introduced. This system, using a soft patch and a sensor-integrated mouthguard, provides comprehensive monitoring of an athlete's hydration and physiological stress levels. A validation study in detecting real-time physiological levels shows the device's performance in capturing moments (400-500 s) of synchronized acute elevation in dehydration (350%) and physiological strain (175%) during field training sessions. Demonstration with a few human subjects highlights the system's capability to detect early signs of health abnormality, thus improving the healthcare of sports athletes.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Recent Progress in Biosensors for Depression Monitoring-Advancing Personalized Treatment

Depression is currently a major contributor to unnatural deaths and the healthcare burden globally, and a patient's battle with depression is often a long one. Because the causes, symptoms, and effects of medications are complex and highly individualized, early identification and personalized treatment of depression are key to improving treatment outcomes. The development of wearable electronics, machine learning, and other technologies in recent years has provided more possibilities for the realization of this goal. Conducting regular monitoring through biosensing technology allows for a more comprehensive and objective analysis than previous self-evaluations. This includes identifying depressive episodes, distinguishing somatization symptoms, analyzing etiology, and evaluating the effectiveness of treatment programs. This review summarizes recent research on biosensing technologies for depression. Special attention is given to technologies that can be portable or wearable, with the potential to enable patient use outside of the hospital, for long periods.

Flexible Thermoelectric Wearable Architecture for Wireless Continuous Physiological Monitoring

Continuous monitoring of physiological signals from the human body is critical in health monitoring, disease diagnosis, and therapeutics. Despite the needs, the existing wearable medical devices rely on either bulky wired systems or battery-powered devices needing frequent recharging. Here, we introduce a wearable, self-powered, thermoelectric flexible system architecture for wireless portable monitoring of physiological signals without recharging batteries. This system harvests an exceptionally high open circuit voltage of 175-180 mV from the human body, powering the wireless wearable bioelectronics to detect electrophysiological signals on the skin continuously. The thermoelectric system shows long-term stability in performance for 7 days with stable power management. Integrating screen printing, laser micromachining, and soft packaging technologies enables a multilayered, soft, wearable device to be mounted on any body part. The demonstration of the self-sustainable wearable system for detecting electromyograms and electrocardiograms captures the potential of the platform technology to offer various opportunities for continuous monitoring of biosignals, remote health monitoring, and automated disease diagnosis.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC's implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants' homes. Our results demonstrate HEROIC's capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Advances in Energy Harvesting Technologies for Wearable Devices

The development of wearable electronics is revolutionizing human health monitoring, intelligent robotics, and informatics. Yet the reliance on traditional batteries limits their wearability, user comfort, and continuous use. Energy harvesting technologies offer a promising power solution by converting ambient energy from the human body or surrounding environment into electrical power. Despite their potential, current studies often focus on individual modules under specific conditions, which limits practical applicability in diverse real-world environments. Here, this review highlights the recent progress, potential, and technological challenges in energy harvesting technology and accompanying technologies to construct a practical powering module, including power management and energy storage devices for wearable device developments. Also, this paper offers perspectives on designing next-generation wearable soft electronics that enhance quality of life and foster broader adoption in various aspects of daily life.

Wireless Technologies in Flexible and Wearable Sensing: From Materials Design, System Integration to Applications

Wireless and wearable sensors attract considerable interest in personalized healthcare by providing a unique approach for remote, noncontact, and continuous monitoring of various health-related signals without interference with daily life. Recent advances in wireless technologies and wearable sensors have promoted practical applications due to their significantly improved characteristics, such as reduction in size and thickness, enhancement in flexibility and stretchability, and improved conformability to the human body. Currently, most researches focus on active materials and structural designs for wearable sensors, with just a few exceptions reflecting on the technologies for wireless data transmission. This review provides a comprehensive overview of the state-of-the-art wireless technologies and related studies on empowering wearable sensors. The emerging functional nanomaterials utilized for designing unique wireless modules are highlighted, which include metals, carbons, and MXenes. Additionally, the review outlines the system-level integration of wireless modules with flexible sensors, spanning from novel design strategies for enhanced conformability to efficient transmitting data wirelessly. Furthermore, the review introduces representative applications for remote and noninvasive monitoring of physiological signals through on-skin and implantable wireless flexible sensing systems. Finally, the challenges, perspectives, and unprecedented opportunities for wireless and wearable sensors are discussed.

Cloud-Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women

Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long-term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at-home cardiovascular monitoring of postpartum women-a group with urgently unmet NCD needs in the United States-using a cloud-integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real-time waveform analytics, including medical device-grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month-long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision-making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.

Intelligent, Flexible Artificial Throats with Sound Emitting, Detecting, and Recognizing Abilities

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values.

AR-Enabled Persistent Human-Machine Interfaces via a Scalable Soft Electrode Array

Augmented reality (AR) is a computer graphics technique that creates a seamless interface between the real and virtual worlds. AR usage rapidly spreads across diverse areas, such as healthcare, education, and entertainment. Despite its immense potential, AR interface controls rely on an external joystick, a smartphone, or a fixed camera system susceptible to lighting. Here, an AR-integrated soft wearable electronic system that detects the gestures of a subject for more intuitive, accurate, and direct control of external systems is introduced. Specifically, a soft, all-in-one wearable device includes a scalable electrode array and integrated wireless system to measure electromyograms for real-time continuous recognition of hand gestures. An advanced machine learning algorithm embedded in the system enables the classification of ten different classes with an accuracy of 96.08%. Compared to the conventional rigid wearables, the multi-channel soft wearable system offers an enhanced signal-to-noise ratio and consistency over multiple uses due to skin conformality. The demonstration of the AR-integrated soft wearable system for drone control captures the potential of the platform technology to offer numerous human-machine interface opportunities for users to interact remotely with external hardware and software.

Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System

Hand function rehabilitation training typically requires monitoring the activation status of muscles directly related to hand function. However, due to factors such as the small surface area for hand-back electrode placement and significant skin deformation, the continuous real-time monitoring of high-quality surface electromyographic (sEMG) signals on the hand-back skin still poses significant challenges. We report a stretchable, flexible, breathable, and self-adhesive epidermal sEMG sensor system. The optimized serpentine structure exhibits a sufficient stretchability and filling ratio, enabling the high-quality monitoring of signals. The carving design minimizes the distribution of connecting wires, providing more space for electrode reservation. The low-cost fabrication design, combined with the cauterization design, facilitates large-scale production. Integrated with customized wireless data acquisition hardware, it demonstrates the real-time multi-channel sEMG monitoring capability for muscle activation during hand function rehabilitation actions. The sensor provides a new tool for monitoring hand function rehabilitation treatments, assessing rehabilitation outcomes, and researching areas such as prosthetic control.

Skin preparation-free, stretchable microneedle adhesive patches for reliable electrophysiological sensing and exoskeleton robot control

High-fidelity and comfortable recording of electrophysiological (EP) signals with on-the-fly setup is essential for health care and human-machine interfaces (HMIs). Microneedle electrodes allow direct access to the epidermis and eliminate time-consuming skin preparation. However, existing microneedle electrodes lack elasticity and reliability required for robust skin interfacing, thereby making long-term, high-quality EP sensing challenging during body movement. Here, we introduce a stretchable microneedle adhesive patch (SNAP) providing excellent skin penetrability and a robust electromechanical skin interface for prolonged and reliable EP monitoring under varying skin conditions. Results demonstrate that the SNAP can substantially reduce skin contact impedance under skin contamination and enhance wearing comfort during motion, outperforming gel and flexible microneedle electrodes. Our wireless SNAP demonstration for exoskeleton robot control shows its potential for highly reliable HMIs, even under time-dynamic skin conditions. We envision that the SNAP will open new opportunities for wearable EP sensing and its real-world applications in HMIs.

Telemedicine for obstructive sleep apnea syndrome: An updated review

Telemedicine (TM) is a new medical service model in which computer, communication, and medical technologies and equipment are used to provide "face-to-face" communication between medical personnel and patients through the integrated transmission of data, voice, images, and video. This model has been increasingly applied to the management of patients with sleep disorders, including those with obstructive sleep apnea syndrome (OSAS). TM technology plays an important role in condition monitoring, treatment compliance, and management of OSAS cases. Herein, we review the concept of TM, its application to OSAS, and the related effects and present relevant application suggestions and strategies, which may provide concepts and references for OSAS-related TM development and application.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model

The energy harvesters used in self-powered wireless sensing technology, which has the potential to completely solve the power supply problem of the sensing nodes from the source, usually require mechanical movement or operate in harsh environments, resulting in a significant reduction in device lifespan and reliability. Therefore, the influencing factors and failure mechanisms of the operating status of self-powered wireless sensors were analyzed, and an innovative evaluation index system was proposed, which includes 4 primary indexes and 13 secondary indexes, including energy harvesters, energy management circuits, wireless communication units, and sensors. Next, the weights obtained from the subjective analytic hierarchy process (AHP) and objective CRITIC weight method were fused to obtain the weights of each index. A self-powered sensor evaluation scheme (FE-SPS) based on fuzzy comprehensive evaluation was implemented by constructing a fuzzy evaluation model. The advantage of this scheme is that it can determine the current health status of the system based on its output characteristics. Finally, taking vibration energy as an example, the operational status of the self-powered wireless sensors after 200 h of operation was comprehensively evaluated. The experimental results show that the test self-powered wireless sensor had the highest score of "normal", which is 0.4847, so the evaluation result was "normal". In this article, a reliability evaluation strategy for self-powered wireless sensor was constructed to ensure the reliable operation of self-powered wireless sensors.

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.