Wearable Devices Research Articles

Pasteables: A Flexible and Smart “Stick-and-Peel” Wearable Platform for Fitness and Athletics

Wearable technologies are becoming increasingly popular and transforming our fitness and sports industry. Monitoring health parameters and body activity can help achieve fitness goals, improve sports performance, and aid in physiotherapy. However, state-of-the-art wearable devices are often not flexible, expensive, hard to set up, or have privacy concerns. They are designed and optimized for a specific application with tight hardware-software integration and it is hard or impossible to re-purpose them for diverse use cases. In this paper, we propose a flexible, re-configurable human body movement and health monitoring platform, called "Pasteables". It is a stick-and-peel device (which acts like a band-aid) that attaches to the skin or clothing and can create an on-body network of smart wearables. Given an application, Pasteables can be easily adapted to its requirements while ensuring good performance and privacy. We present the overall architecture, system design steps, and the configuration process. We have designed a set of functional prototypes and configured them for performing a variety of camera-less pose and posture detection tasks, which are important for athletes, professional dancing, physiotherapy, and fitness workouts. We note that the Pasteables can perform pose/posture detection without external stationary reference at low cost and high accuracy.

Feasibility of Acoustic Features of Vowel Sounds in Estimating the Upper Airway Cross Sectional Area during Wakefulness: A Pilot Study

Assessment of upper airway dimensions has shown great promise in understanding the pathogenesis of obstructive sleep apnea (OSA). However, the current screening system for OSA does not have an objective assessment of the upper airway. The assessment of the upper airway can accurately be performed by MRI or CT scans, which are costly and not easily accessible. Acoustic pharyngometry or Ultrasonography could be less expensive technologies, but these require trained personnel which makes these technologies not easily accessible, especially when assessing the upper airway in a clinic environment or before surgery. In this study, we aimed to investigate the utility of vowel articulation in assessing the upper airway dimension during normal breathing. To accomplish that, we measured the upper airway cross-sectional area (UA-XSA) by acoustic pharyngometry and then asked the participants to produce 5 vowels for 3 seconds and recorded them with a microphone. We extracted 710 acoustic features from all vowels and compared these features with UA-XSA and developed regression models to estimate the UA-XSA. Our results showed that Mel frequency cepstral coefficients (MFCC) were the most dominant features of vowels, as 7 out of 9 features were from MFCC in the main feature set. The multiple regression analysis showed that the combination of the acoustic features with the anthropometric features achieved an R2 of 0.80 in estimating UA-XSA. The important advantage of acoustic analysis of vowel sounds is that it is simple and can be easily implemented in wearable devices or mobile applications. Such acoustic-based technologies can be accessible in different clinical settings such as the intensive care unit and can be used in remote areas. Thus, these results could be used to develop user-friendly applications to use the acoustic features and demographical information to estimate the UA-XSA.

Enhancing stress detection in wearable IoT devices using federated learning and LSTM based hybrid model

Enhancing stress detection in wearable IoT devices using federated learning and LSTM based hybrid model

Double network hydrogel confined MXene/Liquid metal by dynamic hydrogen bond for high-performance wearable sensors

Double-network (DN) hydrogels, which integrate long-chain and short-chain molecular structures, exhibit significant potential in wearable sensors due to their exemplary mechanical properties. However, conventional hydrogels often suffer from poor conductivity and low sensitivity. In this study, we develop a novel DN hydrogel (comprising polyacrylamide/sodium alginate) that encapsulates MXene (Ti3C2Tx) and liquid metal (LM). This configuration leverages the conductive properties of two-dimensional MXene and zero-dimensional liquid metal embedded within the DN hydrogel networks. The Ti3C2Tx MXene sheets enhance the binding capacity with LM by serving as a conductive bridge, thereby improving interfacial interactions and reducing hysteresis. The resultant strain sensor, fabricated from this composite DN hydrogel, achieves high sensitivity (gauge factor up to 12.84), a broad detection range (0 %-1500 %), rapid response time (262 ms), and excellent repeatability and stability. We have integrated this strain sensor into wearable flexible devices, such as contact lenses, enabling real-time monitoring of human physiological pressures.

Electro-capacitive cancer therapy using wearable electric field detector: a review

Electro-capacitive cancer therapy (ECCT), a less invasive and more targeted approach using wearable electric field detectors, is revolutionizing cancer therapy, a complex process involving traditional methods like surgery, chemotherapy, and radiation. The review aims to investigate the safety and efficacy of electric field exposure in vital organs, particularly in cancer therapy, to improve medical advancements. It will investigate the impact on cytokines and insulation integrity, as well as contribute to improving diagnostic techniques and safety measures in medical and engineering fields. Wearable electric field detectors have revolutionized cancer therapy by offering a non-invasive and personalized approach to treatment. These devices, such as smart caps or patches, measure changes in electric fields by detecting capacitance alterations. Their lightweight, comfortable, and easy to-wear nature allows for real-time monitoring, providing valuable data for personalized treatment plans. The portability of wearable detectors allows for long-term surveillance outside clinical settings, increasing therapy efficacy. The ability to collect data over extended periods provides a comprehensive view of electric field dynamics, aiding researchers in understanding tumor growth and progression. Technology advancements in electro-capacitive therapy, including wearable devices, have revolutionized cancer treatment by adjusting electric field intensity in real-time, enhancing personalized medicine, and improving treatment outcomes and patient quality of life.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

The future of wearable health technology: From monitoring to preventive healthcare

Wearable health technology has undergone rapid development, evolving from simple fitness trackers to sophisticated medical devices that provide real-time monitoring and data collection. As these technologies become increasingly integrated into healthcare systems, their potential to shift from reactive treatment to proactive preventive care is gaining attention. This review paper explores the current state of wearable health devices, including the key technologies that enable monitoring and data analysis, and discusses the latest advancements such as AI integration, smart fabrics, and implantable wearables. The paper also delves into the significant role these technologies play in preventive healthcare by enabling continuous monitoring, early disease detection, and personalized health interventions. Furthermore, it addresses the challenges facing wearables, including issues of data accuracy, privacy, and user compliance. Finally, the paper explores future directions and ethical considerations, emphasizing the potential for wearables to reshape healthcare by promoting more accessible, equitable, and preventive healthcare solutions.

The intersection of clinical trial management and patient advocacy: How research professionals can promote patient rights while upholding clinical excellence

The purpose of this study was to investigate the intersection of clinical trial management and patient advocacy, focusing on how research professionals can promote patient rights while upholding clinical excellence. As clinical trials evolve to become more patient-centered, balancing ethical obligations with scientific rigor remains a significant challenge. This paper explores key concepts such as informed consent, patient engagement, data privacy, and the ethical responsibilities of research professionals. Additionally, it examines the role of emerging technologies, including mobile health applications, wearable devices, telemedicine, and blockchain, in enhancing patient advocacy and improving trial outcomes. A thorough literature review and analysis were conducted to identify the challenges faced in integrating patient advocacy into clinical trial management. The study also highlights the critical role of research professionals in ensuring that patient rights are respected while addressing the complexities of global clinical trials, where varying ethical standards pose additional challenges. The findings suggest that patient-centric approaches, supported by digital tools, can significantly improve the ethical integrity of clinical trials, ensuring greater inclusivity, accessibility, and retention of participants. However, these innovations also raise concerns regarding data security, necessitating the implementation of robust cybersecurity measures to protect patient confidentiality. In conclusion, the study recommends the adoption of decentralized clinical trials and the harmonization of global regulatory frameworks to ensure consistent ethical standards. Research professionals must prioritize patient welfare while embracing technological advancements that offer opportunities for more efficient and inclusive clinical research. By striking this balance, clinical trials can better serve both scientific progress and the ethical imperatives of patient advocacy.

Telehealth innovations for cardiovascular disease management

Telehealth innovations and wearable technologies are revolutionizing the management of cardiovascular diseases (CVD), providing patients with more accessible and continuous care. The integration of remote monitoring tools and mobile health applications allows for real-time tracking of vital signs, such as heart rate, blood pressure, and oxygen levels, enabling early detection of abnormalities and timely medical interventions. These technologies empower patients to actively participate in their own health management, encouraging adherence to prescribed lifestyle changes, such as increased physical activity and improved dietary habits, which are essential for preventing and controlling CVD. Wearable devices, such as smartwatches and fitness trackers, play a critical role in the continuous monitoring of cardiovascular health. Combined with telehealth platforms, they offer personalized insights and facilitate communication between patients and healthcare providers, enhancing care coordination. This leads to improved patient outcomes, particularly in managing chronic conditions like hypertension and heart failure, where early intervention is key to preventing complications. Additionally, telehealth solutions reduce the need for frequent in-person visits, cutting healthcare costs and making care more convenient and accessible for patients in remote or underserved areas. As telehealth and wearable technologies continue to advance, they hold the potential to further transform cardiovascular care, contributing to more efficient healthcare delivery and better management of cardiovascular risk factors.

Automation and innovation in home hemodialysis machines.

In this review, we discuss the timeline of innovation and technologic development in home hemodialysis (HHD) in the United States and the legislative approvals that accompanied them. The most recently FDA-approved home hemodialysis devices provide features that include on-demand and batch dialysate generation, access disconnect for venous needle dislodgement, touchscreen interface with visual and auditory prompts and animations, drop-in sterilized cartridges with prestrung tubing, hot water disinfection of tubing allowing extended-use, dialysate flow rates as high as 500 ml/min, as well as remote treatment monitoring capabilities. Furthermore, wearable/portable dialysis devices are currently under development to simplify dialysis delivery to patients with end-stage kidney disease. Home hemodialysis devices providing longitudinal hemodialysis across different clinical settings, virtual reality headsets for more personalized training, automated patient support, as well as wearable device development and innovations give hope for a future where home hemodialysis is more accessible and seamless.

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning.

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

Sociodemographic Factors Associated with the Utilization of Digital Health Technologies Among Informal Caregivers: A Nationwide Study in the USA, 2022.

Informal caregivers can leverage digital health technologies to support their own health while also assisting patients, particularly those with mental or physical challenges. This study investigated the sociodemographic factors associated with the use of digital health technology among informal caregivers. Data from the 2022 Health Information National Trends Survey (HINTS) were examined for this cross-sectional study. The survey identified key outcomes related to the use of online medical records, health apps, digital wearable health devices, and the digital sharing of health information with professionals, on social media, or with others facing similar health issues. Sociodemographic factors (gender, race/ethnicity, feelings of one's household income, education, and census division) were also analyzed. Weighted multivariable logistic regression models were employed. A total of 831 individuals were included, representing about 36,960,385 national informal caregivers in 2022. Caregivers with a high school education or less (vs. those with at least some college education) and non-Hispanic Black caregivers (vs. non-Hispanic White caregivers) were significantly less likely to be offered access to online medical records by their healthcare providers. Additionally, online medical record usage was lower among caregivers with high school education or less, but higher among caregivers aged 50-64 (vs. those aged 35-49). Caregivers with a high school education or less were less likely to use health apps and digital wearable health devices, but more likely to share personal health information on social media. Men caregivers, those aged 50-64 and over 65 (vs. the 35-49 age group), and caregivers who were dissatisfied with their income were less likely to use digital wearable health devices. The findings underscore disparities in the utilization of caregivers' digital health technology, particularly in digital wearable health devices. Recognizing and addressing these disparities are crucial for tailoring interventions to enhance equitable access to digital health technology among diverse informal caregiver populations.

Artificial intelligence for automated left ventricular systolic dysfunction detection using a wearable cardiac patch incorporated with synchronized phonocardiogram and electrocardiogram

Abstract Background Left ventricular systolic dysfunction (LVSD) is characterized by a reduced left ventricular ejection fraction (LVEF) and is associated with three times the risk of developing Heart failure (HF). We aimed to test an artificial intelligence (AI) algorithm applied to synchronized phonocardiography (PCG) and electrocardiography (ECG) signals, recorded with a wearable device, to validate its potential as a screening tool for LVSD. Methods Using ECG and PCG data collected from 1960 admitted patients in a hospital, we trained an AI algorithm to detect the LVSD. There are two ways of defining LVSD, one group is defined by LVEF <50% and the other is defined by LVEF 40% as measured by echocardiography. The AI algorithm followed a two-step detection structure. In the first phase, ECG signals were processed by wavelet denoising and baseline wander correction, and PCG signals were converted to the frequency domain via wavelet transformation. We developed a 1D CNN-based U-Net variant model to pinpoint electromechanical activation time (EMAT). Addressing the challenge of processing multimodal signals, we devised a contrastive learning approach that mandates the encoders for different modalities to generate embeddings within the same feature space. This trained encoder, along with the decoder in U-Net, is subsequently fine-tuned for EMAT detection. The AI-EMAT% was obtained by adjusting detected EMAT based on RR interval. For the second phase, random forest regression is used to estimate LVEF, incorporating features such as AI-EMAT% and clinical data, including age and gender. For LVSD detection, we evaluated the performance of methods by bifurcating AI-estimated LVEF% (AI-predict LVEF%) based on thresholds of 40% and 50%, and by directly estimating AI-EMAT% cutoff. The evaluation of LVSD detection algorithm, conducted via 2-fold cross-validation, involves dividing the dataset into two subsets, with each alternately serving as training and testing sets to ensure a fair assessment. Results We recruited 1960 patients (1430 [72.96%] male). 357 (18.21%) had an ejection fraction of 50% or lower, and 157 (8.01%) had an ejection fraction of 40% or lower. In detecting LVSD of LVEF<50%, AI-EMAT% yielded an AUROC of 0.86, sensitivity of 73.7%, and specificity of 80.7%. While AI-predict LVEF% approach resulted in an AUROC of 0.89, sensitivity of 81.8%, and specificity of 81.5%. In detecting LVSD of LVEF<40%, AI-EMAT% yielded an AUROC of 0.89, sensitivity of 85.4%, and specificity of 76.0%. While AI-predict LVEF% outputs resulted in an AUROC of 0.91, sensitivity of 92.4%, and specificity of 76.8%. Conclusions We developed an automated algorithm to detect LVSD using a wearable device that incorporated synchronized PCG and ECG signals, demonstrating robust performance across different subgroups. This approach represents a strategy for automated screening of LV systolic dysfunction, particularly beneficial in resource-limited settings.Baseline and AUROCAI algorithm

Exploring the effects of a real-time cardiac telerehabilitation using wearable devices in people with coronary artery disease following a recent myocardial infarction: a randomised controlled trial

Abstract Background Exercise-based cardiac rehabilitation (CR) is a non-pharmacological multidisciplinary program for individuals after myocardial infarction (MI), that offers multiple health benefits. One of the greatest barriers to CR participation is the travelling distance to the rehabilitation centre. Remotely monitored CR appears to be at least as effective in improving cardiovascular risk factors and exercise capacity as traditional centre-based CR. Nevertheless, the efficacy of a remotely monitored CR in people with recent MI has yet to be examined. Purpose The aim of this randomised controlled trial was to assess the effects of a real-time cardiac telerehabilitation monitored by wearable devices in people with recent MI. Methods Thirty people (8 women, 22 men) with CAD following a recent (i.e., < 4 week) MI were randomly allocated into two groups (online home-based and gym-based groups). The groups underwent a 26-week CR, thrice per week. All patients performed the baseline and 24 weeks follow up measurements where peak oxygen uptake (VO2peak), mean daily steps, distance and calories were assessed. Results The online group showed improved in mean daily steps (P<0.05) and mean daily distance (P<0.05) at 24 weeks compared to the gym-based group. The paired sample t-test showed that all the assessed variables were statistically (P<0.001) improved for both groups at 24 weeks. Pearson’s r demonstrated positive correlations between VO2peak and mean daily distance (r= .375), and negative correlations between VO2peak and muscle (r= -.523) and fat masses (r= -.460). There was no exercise-induced adverse events during the study. Conclusion Our findings might indicate that a real-time online supervised CR exercise programme using wearable technology to monitor the hemodynamic responses in CAD post MI patients is equally effective to a gym-based exercise programme.Individualized scanwatch report

A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device

Abstract Background Wearable and portable devices with ECG capabilities can provide broad access to screening for cardiovascular disorders. However, most are approved for the detection of only atrial fibrillation. Most algorithms for detecting rhythm and conduction disorders are trained and tested on 12-lead ECGs or the lead I of clinical ECGs, with an unclear role for extending the diagnostic range of wearable and portable devices. Moreover, no current algorithms enable cardiovascular diagnosis on data available to end users, who largely have access to the PDF outputs of their data. Purpose To validate a novel artificial intelligence (AI) model trained to identify conduction disorders and arrhythmias on synthetic ECG outputs from lead I data of clinical ECGs, to detect the same disorders on cardiologist-annotated PDF outputs from the Alivecor KardiaMobile single-lead ECGs (Kardia ECGs). Methods We extracted lead I data from 3,076,352 clinical ECGs across 5 hospitals of a large U.S.-based hospital system from 2000-2024 and simulated wearable PDF outputs from commercial devices using a novel plotting approach that replicated the variations in plotted formats. Gold-standard cardiologist written diagnosis statements were processed to generate 8 clinical labels spanning conduction and rhythm disorders, and we developed a multilabel model to predict these labels using an EfficientNet-B3 convolutional neural network. We examined the performance of the model on 24,808 Kardia ECGs annotated by cardiologists. Results The model achieved a high performance on clinical ECGs in a held-out subset of the synthetic lead I ECG printouts resembling wearable outputs (AUC > 0.9 for all 8 labels). In the independent 24,808 Kardia ECGs, annotated by experts, the model achieved high performance across all labels. For the rhythm disorders atrial fibrillation or atrial flutter, premature atrial contraction, and premature ventricular contraction, the model had AUROCs of 0.99, 0.91, and 0.95, respectively. For the conduction disorders 1st-degree AV block and 2nd or 3rd-degree AV blocks, the model had AUROCs of 0.94 and 0.99, respectively. Finally, for supraventricular tachycardias, wide QRS, and paced ECGs, the model had AUROCs of 0.998, 0.98, and 0.98, respectively. Conclusions We demonstrate that a deep learning model trained to identify rhythm and conduction disorders based on synthetic ECGs where lead I data from clinical 12 lead ECGs are used to replicate real-world PDF outputs of wearable ECGs, generalizes to real-world wearable and handheld single-lead devices.

Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms

Abstract Background Amyloid cardiomyopathy (ATTR-CM) is often underdiagnosed, with very few diagnosed before the onset of symptoms, leading to delayed use of disease-modifying therapies that could have substantial prognostic implications early in the disease course. In the US, the disease has a substantially higher prevalence among Black adults, who are further challenged by lower access to healthcare services and lower preventative care, further reducing the ability for clinician-led diagnosis. We sought to develop an approach for identifying ATTR-CM on noisy single-lead ECGs obtainable on portable and wearable ECG devices, allowing broad use in community screening. Purpose To develop a portable/wearable device-adapted AI-ECG algorithm capable of detecting ATTR-CM on noisy single-lead ECGs. Methods We identified patients with ATTR-CM from 2015 through June 2023 across 5 hospitals of a large U.S.-based hospital system using positive bone scintigraphy scans or pharmacotherapy with an approved transthyretin stabilizer. In the development cohort, we used lead I of 12 lead ECGs, commonly captured by portable and wearable ECG devices, to train a novel noise-adapted model explicitly designed to infer information against simulated real-world noises. For this, ECG signals were augmented using random Gaussian real-world noise within four frequency ranges at multiple signal-to-noise ratios. We tested the model in an independent set of cases and matched controls drawn from July through December 2023. Results The development cohort consisted of 1,011 ECGs from 234 patients (median age 79 years [IQR:70-85], 17% women), with 10:1 age- and sex-matched 10,110 controls with 10,110 ECGs (age 79 [IQR:70-85] years 17.7% female). In the independent test set of 139 ECGs from 47 patients (age 80 [75-86] years, 32% women) and matched controls (1390 ECGs and patients) from July through December 2023, the AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for ATTR-CM was 0.90 [95%CI: 0.88-0.92] (A). The AUPRC was 0.44 [0.36-0.53]. The sensitivity and specificity of the model were 0.85 and 0.80. The positive predictive value ranged from 0.12 at a 3% prevalence level to 0.59 at a 25% prevalence (B). Conclusions A novel portable and wearable-devices adapted model demonstrates promising performance for detecting ATTR-CM on single-lead ECGs, representing a more accessible method for screening in community-dwelling adults.

From Lab to Life: Self-Powered Sweat Sensors and Their Future in Personal Health Monitoring.

The rapid development of wearable sweat sensors has demonstrated their potential for continuous, non-invasive disease diagnosis and health monitoring. Emerging energy harvesters capable of converting various environmental energy sources-biomechanical, thermal, biochemical, and solar-into electrical energy are revolutionizing power solutions for wearable devices. Based on self-powered technology, the integration of the energy harvesters with wearable sweat sensors can drive the device for biosensing, signal processing, and data transmission. As a result, self-powered sweat sensors are able to operate continuously without external power or charging, greatly facilitating the development of wearable electronics and personalized healthcare. This review focuses on the recent advances in self-powered sweat sensors for personalized healthcare, covering sweat sensors, energy harvesters, energy management, and applications. The review begins with the foundations of wearable sweat sensors, providing an overview of their detection methods, materials, and wearable devices. Then, the working mechanism, structure, and a characteristic of different types of energy harvesters are discussed. The features and challenges of different energy harvesters in energy supply and energy management of sweat sensors are emphasized. The review concludes with a look at the future prospects of self-powered sweat sensors, outlining the trajectory of the field and its potential to flourish.

Recent Advances in Organic Photodetectors

Organic photodetectors (OPDs) have garnered significant attention in fields such as image sensing, health monitoring, and wearable devices due to their exceptional performance. This review summarizes recent research advancements in materials, structures, performance, and applications of narrowband organic photodetectors, hybrid organic–inorganic perovskite photodetectors, flexible organic photodetectors (FOPDs), and photomultiplication type organic photodetectors (PM-OPDs). Organic semiconductors offer substantial potential in optoelectronic devices owing to their low cost, ease of processing, and tunable spectral response. Hybrid perovskite materials extend the spectral response range, FOPDs meet the demands of wearable devices, and PM-OPDs enhance sensitivity, allowing for the detection of weak light signals. Through innovations in materials, structural optimization, and improvements in manufacturing processes, the performance of OPDs has seen significant enhancement. This article also explores the application prospects of these technologies in medical monitoring, optical communications, and image sensing.