Search Results (1,077)

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL’s limitations. Successfully addressing these hurdles is essential for enhancing FL’s efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem. Full article

Wearable devices have potential applications in health monitoring and personalized healthcare due to their portability, conformability, and excellent mechanical flexibility. However, their performance is often limited by instability in acidic or basic environments. In this study, a flexible sensor with excellent stability based on a GaN nanoplate was developed through a simple and controllable fabrication process, where the linearity and stability remained at almost 99% of the original performance for 40 days in an air atmosphere. Moreover, perfect stability was also demonstrated in acid–base environments, with pH values ranging from 1 to 13. Based on its excellent stability and piezotronic performance, a flexible device for motion monitoring was developed, capable of detecting motions such as finger, knee, and wrist bending, as well as swallowing. Furthermore, gesture recognition and intelligent fall monitoring were explored based on the bending properties. In addition, an intelligent fall warning system was proposed for the personalized healthcare application of elders by applying machine learning to analyze data collected from typical activities. Our research provides a path for stable and flexible electronics and personalized healthcare applications. Full article

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges. Full article

Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain–computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices. Full article

This study evaluates the current scope of smart technology applications that support aging in place and identifies potential avenues for future research. The global demographic shift towards an aging population has intensified interest in technologies that enable older adults to maintain independence and quality of life within their homes. We conducted a systematic review of the scientific literature from Web of Science, PubMed, and ProQuest, identifying 44 smart technologies across 32 publications. These technologies were classified into three categories: nonmobile technologies for individual monitoring, nonmobile technologies for home environment monitoring, and wearable technologies for health and activity tracking. Notably, the research in this area has grown significantly since 2018; yet, notable gaps persist, particularly within the traditional disciplines related to aging and in the use of quantitative methodologies. This emerging field presents substantial opportunities for interdisciplinary research and methodological advancement, highlighting the need for well-developed research strategies to support the effective integration of smart technology in aging in place. Full article

Sarcopenia has been a serious concern in the context of an increasingly aging global population. Existing detection methods for sarcopenia are severely constrained by cumbersome devices, the necessity for specialized personnel, and controlled experimental environments. In this study, we developed an innovative wearable fabric system based on conductive fabric and flexible sensor array. This fabric system demonstrates remarkable pressure-sensing capabilities, with a high sensitivity of 18.8 kPa⁻¹ and extraordinary stability. It also exhibits excellent flexibility for wearable applications. By interacting with different parts of the human body, it facilitates the monitoring of various physiological activities, such as pulse dynamics, finger movements, speaking, and ambulation. Moreover, this fabric system can be seamlessly integrated into sole to track critical indicators of sarcopenia patients, such as walking speed and gait. Clinical evaluations have shown that this fabric system can effectively detect variations in indicators relevant to sarcopenia patients, proving that it offers a straightforward and promising approach for the diagnosis and assessment of sarcopenia. Full article

Background/Objectives: Sudden cardiac death (SCD) remains a major global health concern and represents one of the most common causes of mortality due to cardiovascular diseases. The wearable cardioverter–defibrillator (WCD) is an innovative, non-invasive medical device designed to provide continuous heart monitoring and immediate defibrillation in patients at risk for SCD. The study aimed to assess the efficacy of WCD usage in patients awaiting decision on therapy with implantable cardioverter–defibrillators (ICDs). Methods: We explored the clinical applications, benefits, and limitations of WCD usage within the BIA-VEST registry in Poland over the years 2021–2023. The study included 10 patients with a mean age of 49.1 ± 12.02 years. Results: All patients demonstrated good tolerance and compliance with the LifeVest WCD, wearing it for an average of 93.1 days, about 22.8 h per day (95.7% of the time). No interventions from LifeVests were recorded, and there were no effective, ineffective, or inadequate discharges. After the first follow-up echocardiography, five patients still required ICDs. Due to improved LVEF and overall condition in six out of ten patients undergoing WCD bridge therapy, ICD implantation was finally waived. Conclusions: The WCD acts as a bridge to therapy, such as ICD implantation or cardiac surgery, and may be particularly beneficial for patients with transient or evolving conditions, such as structural heart diseases and life-threatening ventricular arrhythmias. Full article

Various electrospinning techniques can be used to produce nanofiber mats with randomly oriented or aligned nanofibers made of different materials and material mixtures. Such nanofibers have a high specific surface area, making them sensitive as sensors for health monitoring. The entire nanofiber mats are very thin and lightweight and, therefore, can be easily integrated into wearables such as textile fabrics or even patches. Nanofibrous sensors can be used not only to analyze sweat but also to detect physical parameters such as ECG or heartbeat, movements, or environmental parameters such as temperature, humidity, etc., making them an interesting alternative to other wearables for continuous health monitoring. This paper provides an overview of various nanofibrous sensors made of different materials that are used in health monitoring. Both the advantages of electrospun nanofiber mats and their potential problems, such as inhomogeneities between different nanofiber mats or even within one electrospun specimen, are discussed. Full article

The demand for non-invasive, real-time health monitoring has driven advancements in wearable sensors for tracking biomarkers in sweat. Ammonium ions (NH₄⁺) in sweat serve as indicators of metabolic function, muscle fatigue, and kidney health. Although current ion-selective all-solid-state printed sensors based on nanocomposites typically exhibit good sensitivity (~50 mV/log [NH₄⁺]), low detection limits (LOD ranging from 10⁻⁶ to 10⁻⁷ M), and wide linearity ranges (from 10⁻⁵ to 10⁻¹ M), few have reported the stability test results necessary for their integration into commercial products for future practical applications. This study presents a highly stable, wearable electrochemical sensor based on a composite of metal–organic frameworks (MOFs) and reduced graphene oxide (rGO) for monitoring NH₄⁺ in sweat. The synergistic properties of Ni-based MOFs and rGO enhance the sensor’s electrochemical performance by improving charge transfer rates and expanding the electroactive surface area. The MOF/rGO sensor demonstrates high sensitivity, with a Nernstian response of 59.2 ± 1.5 mV/log [NH₄⁺], an LOD of 10^−6.37 M, and a linearity range of 10⁻⁶ to 10⁻¹ M. Additionally, the hydrophobic nature of the MOF/rGO composite prevents water layer formation at the sensing interface, thereby enhancing long-term stability, while its high double-layer capacitance minimizes potential drift (7.2 µV/s (i = ±1 nA)) in short-term measurements. Extensive testing verified the sensor’s exceptional stability, maintaining consistent performance and stable responses across varying NH₄⁺ concentrations over 7 days under ambient conditions. On-body tests further confirmed the sensor’s suitability for the continuous monitoring of NH₄⁺ levels during physical activities. Further investigations are required to fully elucidate the impact of interference from other sweat components (such as K⁺, Na⁺, Ca²⁺, etc.) and the influence of environmental factors (including the subject’s physical activity, posture, etc.). With a clearer understanding of these factors, the sensor has the potential to emerge as a promising tool for wearable health monitoring applications. Full article

In the growing field of personalized medicine, non-invasive wearable devices and sensors are valuable diagnostic tools for the real-time monitoring of physiological and biokinetic signals. Among all the possible multiple (bio)-entities, pH is important in defining health-related biological information, since its variations or alterations can be considered the cause or the effect of disease and disfunction within a biological system. In this work, an innovative (bio)-electrochemical flexible pH sensor was proposed by realizing three electrodes (working, reference, and counter) directly on a polyimide (Kapton) sheet through the implementation of CO₂ laser writing, which locally converts the polymeric sheet into a laser-induced graphene material (LIG electrodes), preserving inherent mechanical flexibility of Kapton. A uniform distribution of nanostructured PEDOT:PSS was deposited via ultrasonic spray coating onto an LIG working electrode as the active material for pH sensing. With a pH-sensitive PEDOT coating, this flexible sensor showed good sensitivity defined through a linear Nernstian slope of (75.6 ± 9.1) mV/pH, across a pH range from 1 to 7. We demonstrated the capability to use this flexible pH sensor during dynamic experiments, and thus concluded that this device was suitable to guarantee an immediate response and good repeatability by measuring the same OCP values in correspondence with the same pH applied. Full article

(1) Background: Continuous health promotion systems are increasingly important, enabling decentralized patient care, providing comfort, and reducing congestion in healthcare facilities. These systems allow for treatment beyond clinical settings and support preventive monitoring. Wearable systems have become essential tools for health monitoring, but they focus mainly on physiological data, overlooking motor data evaluation. The World Health Organization reports that 1.71 billion people globally suffer from musculoskeletal conditions, marked by pain and limited mobility. (2) Methods: To gain a deeper understanding of wearables for the motor rehabilitation, monitoring, and prediction of the progression and/or degradation of symptoms directly associated with upper-limb pathologies, this study was conducted. Thus, all articles indexed in the Web of Science database containing the terms “wearable”, “upper limb”, and (“rehabilitation” or “monitor” or “predict”) between 2019 and 2023 were flagged for analysis. (3) Results: Out of 391 papers identified, 148 were included and analyzed, exploring pathologies, technologies, and their interrelationships. Technologies were categorized by typology and primary purpose. (4) Conclusions: The study identified essential sensory units and actuators in wearable systems for upper-limb physiotherapy and analyzed them based on treatment methods and targeted pathologies. Full article

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article

Transportation pressure poses a serious threat to the health of live sheep and the quality of their meat. So, the edible Hu sheep was chosen as the research object for meat sheep. We constructed a systematic biosignal detecting, processing, and modeling method. The biosignal sensing was performed with wearable sensors (photoelectric sensor and infrared temperature measurement) for physiological dynamic sensing and continuous monitoring of the transport environment of meat sheep. Core waveform extraction and modern spectral estimation methods are used to determine and strip out the target signal waveform from it for the purpose of accurate sensing and the acquisition of key transport parameters. Subsequently, we built a qualitative stress assessment method based on external manifestations with reference to the Karolinska drowsiness scale to establish stage classification rules for monitoring data in the transportation environment of meat sheep. Finally, machine learning algorithms such as Gaussian Naive Bayes (GaussianNB), Passive-Aggressive Aggregative Classifier (PAC), Nearest Centroid (NC), K-Nearest Neighbor Classification (KNN), Random Forest (RF), Support Vector Classification (SVC), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGB) were established to predict the classification models of transportation stress in meat sheep. Their classification results were compared. The results show that SVC and GBDT algorithms are more effective and the overall model classification accuracy reached 86.44% and 91.53%. XGB has the best results. The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. The final results achieved improve transport reliability, reduce transport risk, and solve the problems of inefficient meat sheep transport supervision and quality control. Full article

Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the “wearability” of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. Wearable devices possess significant potential to enhance daily life, yet their success depends on understanding and validating the design factors that influence comfort, usability, and seamless integration into everyday routines. This review aimed to evaluate the “wearability” of smart devices through a mixed-methods scoping literature review. By analyzing studies on comfort, usability, and daily integration, it sought to identify design improvements and research gaps to enhance user experience and system design. From an initial pool of 130 publications (1998–2024), 19 studies met the inclusion criteria. The review identified three significant outcomes: (1) a lack of standardized assessment methods, (2) the predominance of qualitative over quantitative assessments, and (3) limited utility of findings for informing design. Although qualitative studies provide valuable insights, the absence of quantitative research hampers the development of validated, generalizable design criteria. This underscores the urgent need for future studies to adopt robust quantitative methodologies to better assess wearability and inform evidence-based design strategies. Full article

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale. Full article