Personal Health Monitoring Research Articles

Conjugation Linked PEDOT:PSS with Low Impedance and High Stretchability for Epidermal Electrophysiology.

Long-term epidermal recording of bioelectricity is of paramount importance for personal health monitoring. It requires stretchable and dry film electrodes that can be seamlessly integrated with skin. The simultaneous achievement of high conductivity and skin-like ductility of conducting materials is a prerequisite for reliable signal transduction at the dynamic interface, which is also the bottleneck of epidermal electrophysiology. Here, carbon nanotubes (CNTs) are introduced as "conjugation linkers" into a topologically plasticized conducting polymer (PEDOT:PSS). A thin-film electrode with high conductivity (≈3250Scm-1) and high stretchability (crack-onset strain>100%) is obtained. In particular, the conjugation linker enables the high volumetric capacitance and the low film resistance, both of which synergically reduce the interfacial impedance. The capabilities of this electrode is further demonstrated in the precise recording of various electrophysiological signals.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

ChatGPT in orthodontics: limitations and possibilities

Abstract Artificial Intelligence (AI) simulates human intelligence in machines capable of rational response and actions. AI’s growth in healthcare, particularly in dentistry, has been remarkable in aiding clinical practices from appointment scheduling to treatment outcome predictions. In orthodontics, AI effectively handles tasks such as automated landmark detection, diagnosing orthodontic needs, assessing maxillary constriction, and selecting appliances. ChatGPT, an AI-based program by OpenAI, excels in generating human-like responses, making it useful across many applications, including those in dentistry. AI enhances personalised patient communication, supports diagnosis and decision-making, digitises patient data, and aids in disease prevention and treatment. In addition, ChatGPT offers support in personalised care, billing, and patient health monitoring. Patients benefit from its help in finding providers, scheduling initial appointments, accessing health information, improving healthcare access and service effectiveness. However, there are limitations. ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. Despite these issues, if trained properly, ChatGPT could significantly enhance medical knowledge dissemination. Future studies should weigh the benefits of AI against its risks, and users must verify AI-generated information with reliable sources to ensure effective patient care.

Lightweight, robust and conductive MXene/SiO2 nanofiber aerogels for excellent sensing and thermal management properties

Flexible pressure sensors embody outstanding mechanical and sensing performance, particularly in the fields of personal health monitoring and artificial intelligence, while catering to multifunctional applications resilient against harsh weather conditions. Among these, Ti3C2Tx MXene-based aerogel materials with high surface area and excellent conductivity have attracted a lot of attention. However, there are big challenges in improving the mechanical properties at low densities and designing multi-level pore structures. Herein, the prepared silane monomers (MP) are firstly synthesized by 3-(Trimethoxysilyl) propyl methacrylate (MPTES) and polyethylene glycol diacrylate (PEGDA), then polydopamine-modified silicon dioxide (PDA-SiO2) nanofibers employed with MXene nanosheets form covalent cross-linking nanofiber aerogels. The multi-level stable structure improves both mechanical strength and elasticity. The assembled MXene/MP/PDA-SiO2 pressure sensors exhibit a high sensitivity of −0.83 kPa−1, a rapid response time of 48 ms, a minimum 1 % strain detection limit, and reliable stability over 10,000 cycles. Furthermore, these sensors can be used for human motion detection and health diagnostics, and show good personal thermal management performance. This work holds a promising candidate for the next generation of multifunctional flexible electronic devices.

Viscoelastic Adhesive, Super-Conformable, and Semi-Flowable Liquid Metal Eutectogels for High-Fidelity Electrophysiological Monitoring.

Integrating gels with human skin through wearables provides unprecedented opportunities for health monitoring technology and artificial intelligence. However, most conductive hydrogels, organogels, and ionogels lack essential environmental stability, biocompatibility, and adhesion for reliable epidermal sensing. In this study, we have developed a liquid metal eutectogel simultaneously possessing superior viscoelasticity, semiflowability, and mechanical rigidity for low interfacial skin impedance, high skin adhesion, and durability. Liquid metal particles (LMPs) are employed to generate free radicals and gallium ions to accelerate the polymerization of acrylic acid monomers in a deep eutectic solvent (DES), obtaining highly viscoelastic polymer networks via physical cross-linking. In particular, graphene oxide (GO) is utilized to encapsulate the LMPs through a sonication-assisted electrostatic assembly to stabilize the LMPs in DES, which also enhances the mechanical toughness and regulates the rheological properties of the eutectogels. Our optimized semi-flowable eutectogel exhibits viscous fluid behavior at low shear rates, facilitating a highly conformable interface with hairy skin. Simultaneously, it demonstrates viscoelastic behavior at high shear rates, allowing for easy peel-off. These distinctive attributes enable the successful applications of on-skin adhesive strain sensing and high-fidelity human electrophysiological (EP) monitoring, showcasing the versatility of these ionically conductive liquid metal eutectogels in advanced personal health monitoring.

Knitting Elastic Conductive Fibers of MXene/Natural Rubber for Multifunctional Wearable Sensors.

Wearable electronic sensors have recently attracted tremendous attention in applications such as personal health monitoring, human movement detection, and sensory skins as they offer a promising alternative to counterparts made from traditional metallic conductors and bulky metallic conductors. However, the real-world use of most wearable sensors is often hindered by their limited stretchability and sensitivity, and ultimately, their difficulty to integrate into textiles. To overcome these limitations, wearable sensors can incorporate flexible conductive fibers as electrically active components. In this study, we adopt a scalable wet-spinning approach to directly produce flexible and conductive fibers from aqueous mixtures of Ti3C2Tx MXene and natural rubber (NR). The electrical conductivity and stretchability of these fibers were tuned by varying their MXene loading, enabling knittability into textiles for wearable sensors. As individual filaments, these MXene/NR fibers exhibit suitable conductivity dependence on strain variations, making them ideal for motivating sensors. Meanwhile, textiles from knitted MXene/NR fibers demonstrate great stability as capacitive touch sensors. Collectively, we believe that these elastic and conductive MXene/NR-based fibers and textiles are promising candidates for wearable sensors and smart textiles.

Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

With the rapid development of the Internet of Things (IoTs), wearable sensors are playing an increasingly important role in daily monitoring of personal health and wellness. The signal-to-noise-ratio has become the most critical performance factor to consider. To enhance it, on the one hand, good sensing materials/devices have been employed; on the other hand, signal amplification and noise reduction circuits have been used. However, most of these devices and circuits work in an active sampling mode, requiring frequent data acquisition and hence, entailing high-power consumption. In this scenario, a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great interest. Here, we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor (PBJT) that acts as an autonomous and highly sensitive, current- and/or voltage-mediated pressure sensor. The PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collector-base diodes. Upon force exertion on the emitter side, as a result of the piezoelectric effect, the emitter-base diode is forward biased while the collector-base diode is reverse biased. Due to the inherent BJT amplification effect, the PBJT achieves record-high sensitivities of 139.7 kPa-1 (current-based) and 88.66 kPa-1 (voltage-based) in sensing mode. The PBJT also has a fast response time of <110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger touch. Therefore, the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous, making it promising for pressure sensing and other IoT applications.

Development of Optical Nanosensors for Detection of Potassium Ions and Assessment of Their Biocompatibility with Corneal Epithelial Cells.

Imbalance of potassium-ion levels in the body can lead to physiological dysfunctions, which can adversely impact cardiovascular, neurological, and ocular health. Thus, quantitative measurement of potassium ions in a biological system is crucial for personal health monitoring. Nanomaterials can be used to aid in disease diagnosis and monitoring therapies. Optical detection technologies along with molecular probes emitting within the near-infrared (NIR) spectral range are advantageous for biological measurements due to minimal interference from light scattering and autofluorescence within this spectral window. Herein, we report the development of NIR fluorescent nanosensors, which can quantitatively detect potassium ions under biologically relevant conditions. The optical nanosensors were developed by using photoluminescent single-walled carbon nanotubes (SWCNTs) encapsulated in polymers that contain potassium chelating moieties. The nanosensors, polystyrene sulfonate [PSS-SWCNTs, nanosensor 1 (NS1)] or polystyrene-co-polystyrene sulfonate [PS-co-PSS-SWCNTs, nanosensor 2 (NS2)], exhibited dose-dependent optical responses to potassium ion level. The nanosensors demonstrated their biocompatibility via the evaluation of cellular viability, proliferation assays, and expression of cytokeratin 12 in corneal epithelial cells (CEpiCs). Interestingly, the nanosensors' optical characteristics and their responses toward CEpiCs were influenced by encapsulating polymers. NS2 exhibited a 10 times higher fluorescence intensity along with a higher signal-to-noise ratio as compared to NS1. NS2 showed an optical response to potassium ion level in solution within 5 min of addition and a limit of detection of 0.39 mM. Thus, NS2 was used for detailed investigations including potassium ion level detection in serum. NS2 showed a consistent response to potassium ions at the lower millimolar range in serum. These results on optical sensing along with biocompatibility show a great potential for nanotube sensors in biomedical research.

Deep learning for personalized health monitoring and prediction: A review

AbstractPersonalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

Amoeba‐Inspired Self‐Healing Electronic Slime for Adaptable, Durable Epidermal Wearable Electronics

AbstractEpidermal electronics has garnered significant research attention due to its promising applications in wearable human‐machine interfaces and intelligent healthcare sensing. However, their widespread use faces challenges due to complex manufacturing processes, high material costs, inadaptability to different skin surfaces, and inadequate reusability. Herein, inspired by the biological reshapability and environmental adaptability of amoeba, an ultra‐deformable (≈2600% strain), bioadhesive (adhesive strength ≈3 kPa), strong self‐healing (fastest recovery time ≈1s, maximum wound distance ≈5 mm), and electromechanical‐durable wearable electronic slime (E‐slime) is proposed, which can instantaneously form on‐skin electronics in situ to detect body motion and physiological signals. E‐slime demonstrates desired sensing performance with high sensitivity (gauge factor 2.95), wide sensing range (up to 400% strain), and low detection limit (≈1% strain), which can seamlessly adhere to the skin and can be easily reused multiple times (≈100 cycles usage). E‐slime also enables on‐the‐fly deployment of motion monitoring tasks at various body locations, showcasing its versatility and reliability for body motion recognition and personal health monitoring. This study holds potential for next‐generation green electronics, motion sensing devices, and wearable human‐machine interfaces, ultimately helping to ensure healthy lives and promote well‐being.

Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks

An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical business by providing new capabilities such as remote patient monitoring and self-monitoring. This research proposed an IoT-based ECG monitoring system that employs a heart rate sensor to generate data and an intelligent hybrid classification algorithm to categorize the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper describes WISE (Wearable IoT-cloud-based health monitoring system), a one-of-a-kind system for real-time personal health monitoring. WISE makes use of the BASN (body area sensor network) technology to provide real-time health monitoring. WISE rapidly transfers data from the BASN to the cloud, and a lightweight wearable LCD may be included to enable quick access to real-time data. This model can address the issue of class imbalance in the ECG dataset, assisting in the development of an IoT-based smart and accurate healthcare system. Pre-processing, feature extraction, and classification are the three steps in any classification technique, whether it is emotion classification or heart health classification. Sensors are used to collect an ECG signal from a person's outside body. The provided ECG signal is first pre-processed using the Butterworth Filtering Method, which effectively reduces noise from the signal. Following pre-processing, the Adaptive Discrete Wavelet Transform technique is used to anticipate the signal's attributes. Lastly, a decision making classification approach based on relational weights is used to determine if the ECG signal is normal or abnormal.

The EarSAVAS Dataset

Subject-aware vocal activity sensing on wearables, which specifically recognizes and monitors the wearer's distinct vocal activities, is essential in advancing personal health monitoring and enabling context-aware applications. While recent advancements in earables present new opportunities, the absence of relevant datasets and effective methods remains a significant challenge. In this paper, we introduce EarSAVAS, the first publicly available dataset constructed specifically for subject-aware human vocal activity sensing on earables. EarSAVAS encompasses eight distinct vocal activities from both the earphone wearer and bystanders, including synchronous two-channel audio and motion data collected from 42 participants totaling 44.5 hours. Further, we propose EarVAS, a lightweight multi-modal deep learning architecture that enables efficient subject-aware vocal activity recognition on earables. To validate the reliability of EarSAVAS and the efficiency of EarVAS, we implemented two advanced benchmark models. Evaluation results on EarSAVAS reveal EarVAS's effectiveness with an accuracy of 90.84% and a Macro-AUC of 89.03%. Comprehensive ablation experiments were conducted on benchmark models and demonstrated the effectiveness of feedback microphone audio and highlighted the potential value of sensor fusion in subject-aware vocal activity sensing on earables. We hope that the proposed EarSAVAS and benchmark models can inspire other researchers to further explore efficient subject-aware human vocal activity sensing on earables.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Highly Stretchable Double Network Ionogels for Monitoring Physiological Signals and Detecting Sign Language.

At the heart of the non-implantable electronic revolution lies ionogels, which are remarkably conductive, thermally stable, and even antimicrobial materials. Yet, their potential has been hindered by poor mechanical properties. Herein, a double network (DN) ionogel crafted from 1-Ethyl-3-methylimidazolium chloride ([Emim]Cl), acrylamide (AM), and polyvinyl alcohol (PVA) was constructed. Tensile strength, fracture elongation, and conductivity can be adjusted across a wide range, enabling researchers to fabricate the material to meet specific needs. With adjustable mechanical properties, such as tensile strength (0.06-5.30 MPa) and fracture elongation (363-1373%), this ionogel possesses both robustness and flexibility. This ionogel exhibits a bi-modal response to temperature and strain, making it an ideal candidate for strain sensor applications. It also functions as a flexible strain sensor that can detect physiological signals in real time, opening doors to personalized health monitoring and disease management. Moreover, these gels' ability to decode the intricate movements of sign language paves the way for improved communication accessibility for the deaf and hard-of-hearing community. This DN ionogel lays the foundation for a future in which e-skins and wearable sensors will seamlessly integrate into our lives, revolutionizing healthcare, human-machine interaction, and beyond.

WHO USES MHEALTH IN HYPERTENSION CARE? - INSIGHTS FROM A QUANTITATIVE SURVEY AMONG HYPERTENSION APP USERS

Objective: Digital health technologies, particularly mHealth apps, are proving to be effective tools for both prevention and monitoring of hypertension therapy. The aim of this study is to gain an in-depth understanding of the profiles and adoption patterns of mHealth users in hypertension care. Design and method: From January to October 2023, a quantitative, standardized questionnaire-survey was conducted with users of a hypertension-focused mHealth app (Hypertension.App). The data analysis included descriptive statistics as well as Bayesian univariate and multivariate logistic regressions. The study is part of the Innovation Fund Project DiPaH. Results: Overall, 255 questionnaires were included in the analysis. The mean age of the app users was 53.6 ± 13.9 years. Most participants were male (54.3%, 139/255), 42.9% (109/255) were female and 0.79% (2/255) non-binary. Most participants had a university degree (44.5%, 113/255) or high school diploma (22%, 56/255). The majority reported that they live in an urban environment (57.8%, 147/255). Overall, 44.5% (113/255) of the participants reported a monthly net income of more than 2500 € per month and 20.1% (51/255) an income between 1500-2500€ per month. The mean eHEALS score (eHealth literacy / 1-40) was 31.0 ± 4.91. Most users received a hypertension diagnosis more than 10 years ago (60.2%, 153/255) or more than 10 years ago. The most common method of discovering the app was personal searches on the web or app store (44.1%, 112/255). The majority reported using the app at least once a week (80.7%, 205/255), with daily usage reported by 52.4% (133/255). However, the app's integration into formal medical treatment was limited, with only 11.8% (30/255) of participants having their medication adjusted based on app entries. Conclusions: The high frequency of app usage suggests that users find it a valuable tool in monitoring their blood pressure. The results indicate that people with a high level of education, high income and in urban regions are more likely to use mHealth in hypertension care. Additionally, the limited integration of the app into medical treatment indicates a gap between personal health monitoring and formal healthcare.

Research Progress and Application of Multimodal Flexible Sensors for Electronic Skin.

In recent years, wearable electronic skin has garnered significant attention due to its broad range of applications in various fields, including personal health monitoring, human motion perception, human-computer interaction, and flexible display. The flexible multimodal sensor, as the core component of electronic skin, can mimic the multistimulus sensing ability of human skin, which is highly significant for the development of the next generation of electronic devices. This paper provides a summary of the latest advancements in multimodal sensors that possess two or more response capabilities (such as force, temperature, humidity, etc.) simultaneously. It explores the relationship between materials and multiple sensing capabilities, focusing on both active materials that are the same and different. The paper also discusses the preparation methods, device structures, and sensing properties of these sensors. Furthermore, it introduces the applications of multimodal sensors in human motion and health monitoring, as well as intelligent robots. Finally, the current limitations and future challenges of multimodal sensors will be presented.

Flexible piezoelectric sensor based on PAN/MXene/PDA@ZnO composite film for human health and motion detection with fast response and highly sensitive

Polyacrylonitrile (PAN) nanofiber-based flexible piezoelectric sensors are recognized for their applicability in wearable electronic devices, personal health monitoring, and motion detection. This research explores the impact of dual fillers, MXene and polydopamine-modified zinc oxide (PDA@ZnO), on the characteristics of PAN-based piezoelectric composite nanofiber films, including planar zigzag conformation content, dielectric, ferroelectric, piezoelectric, and mechanical properties. By integrating PDA@ZnO as a filler, PAN/MXene/PDA@ZnO-5 (PMPO) piezoelectric sensors were developed, showcasing enhanced piezoelectric sensing capabilities. These sensors achieved a sensitivity of 28.56 V/N across a broad linear range, with superior mechanical stability and durability across 3000 loading–unloading cycles, and exhibited swift response and recovery times of 49 ms and 40 ms, respectively. Furthermore, their unique fiber structure and flexibility, when interfaced with the human body, enable the detection of minute physiological movements and the conversion of varied motions into distinct voltage outputs. Thus, these sensors offer considerable promise for applications in human health monitoring and motion posture correction.

Recent Advances in Wearable Healthcare Devices: From Material to Application.

In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

Direct Laser Processing and Functionalizing PI/PDMS Composites for an On-Demand, Programmable, Recyclable Device Platform.

Skin-interfaced high-sensitive biosensing systems to detect electrophysiological and biochemical signals have shown great potential in personal health monitoring and disease management. However, the integration of 3D porous nanostructures for improved sensitivity and various functional composites for signal transduction/processing/transmission often relies on different materials and complex fabrication processes, leading to weak interfaces prone to failure upon fatigue or mechanical deformations. The integrated system also needs additional adhesive to strongly conform to the human skin, which can also cause irritation, alignment issues, and motion artifacts. This work introduces a skin-attachable, reprogrammable, multifunctional, adhesive device patch fabricated by simple and low-cost laser scribing of an adhesive composite with polyimide powders and amine-based ethoxylated polyethylenimine dispersed in the silicone elastomer. The obtained laser-induced graphene in the adhesive composite can be further selectively functionalized with conductive nanomaterials or enzymes for enhanced electrical conductivity or selective sensing of various sweat biomarkers. The possible combination of the sensors for real-time biofluid analysis and electrophysiological signal monitoring with RF energy harvesting and communication promises a standalone stretchable adhesive device platform based on the same material system and fabrication process.

Wearable Sensing Device Integrated with Prestored Reagents for Cortisol Detection in Sweat.

Wearable sweat sensors have achieved rapid development since they hold great potential in personalized health monitoring. However, a typical difficulty in practical processes is the control of working conditions for biorecognition elements, e.g., pH level and ionic strength in sweat may decrease the affinity between analytes and recognition elements. Here, we developed a wearable sensing device for cortisol detection in sweat using an aptamer as the recognition element. The device integrated functions of sweat collection, reagent prestorage, and signal conversion. Especially, the components of prestored reagents were optimized according to the inherent characteristics of sweat samples and electrodes, which allowed us to keep optimal conditions for aptamers. The sweat samples were transferred from the inlet of the device to the reagent prestored chamber, and the dry preserved reagents were rehydrated with sweat and then arrived at the aptamer-modified electrodes. Sweat samples of volunteers were analyzed by the wearable sensing device, and the results showed a good correlation with those of the ELISA kit. We believe that this convenient and reliable wearable sensing device has significant potential in self-health monitoring.