Search Results (8,326)

The triboelectric nanogenerator (TENG) has demonstrated exceptional efficiency in harvesting diverse forms of mechanical energy and converting it into electrical energy. This technology is particularly valuable for powering low-energy electronic devices and self-powered sensors. Most traditional TENGs use single-sided patterned friction pairs, which restrict their effective contact area and overall performance. Here, we propose a novel TENG that incorporates microwave patterned aluminum (MC-Al) foil and microcone structured polydimethylsiloxane (MC-PDMS). This innovative design utilizes two PMMA molds featuring identical micro-hole arrays ablated by a CO₂ laser, making it both cost-effective and easy to fabricate. A novel room imprinting technique has been employed to create the micromorphology of aluminum (Al) foil using the PMMA mold with shallower micro-hole arrays. Compared to TENGs with flat friction layers and single-side-patterned friction layers, the double-side-patterned MW-MC-TENG demonstrates superior output performance due to increased cone deformation and contact area. The open-circuit voltage of the MW-MC-TENG can reach 141 V, while the short-circuit current can attain 71.5 μA, corresponding to a current density of 2.86 µA/cm². The power density reaches 1.4 mW/cm² when the resistance is 15 MΩ, and it can charge a 0.1 μF capacitor to 2.01 V in 2.28 s. In addition, the MW-MC-TENG can function as an insole device to harvest walking energy, power 11 LED bulbs, monitor step speed, and power a timer device. Therefore, the MW-MC-TENG has significant application potential in micro-wearable devices. Full article

A highly sensitive optical fiber gas pressure sensor with temperature monitoring is proposed and demonstrated. It is based on a slightly tapered fiber with an inner micro-cavity forming an in-fiber Mach–Zehnder interferometer (MZI), and a micro-channel is drilled into the lateral wall of the in-fiber micro-cavity using a femtosecond laser to allow gas to flow in. Due to the dependence of the refractive index (RI) of air inside the micro-cavity on its gas pressure and the high RI sensitivity of the MZI, the device is extremely sensitive to gas pressure. To prevent fiber breakage, the MZI is housed in a silicate capillary tube with an air inlet. Multiple modes are excited by slightly tapering the inner micro-cavity, and the resonance dips in the sensor’s transmission spectrum feature different linear gas pressure and temperature responses, so a sensitivity matrix algorithm can be used to achieve simultaneous demodulation of two parameters, thus resolving the temperature crosstalk. As expected, the experimental results demonstrated the reliability of the matrix algorithm, with pressure sensitivity reaching up to ~−12.967 nm/MPa and temperature sensitivity of ~89 pm/°C. The features of robust mechanical strength and high air pressure sensitivity with temperature monitoring imply that the proposed sensor has good practical and application prospects. Full article

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management. Full article

Bud Rot (BR) is the most significant phytosanitary threat to oil palm cultivation in Colombia. Early detection is essential for effective curative management, but current methods for detecting BR in adult palms are subjective and unreliable. This research aimed to develop an integrated system for digital field monitoring and image analysis, testing two detection methods: computer-assisted detection and automatic detection using artificial intelligence (AI). Monthly monitoring was conducted over a 12-month period (January–December 2022) on 672 African oil palms (Elaeis guineensis), 15 years old and susceptible to BR. Disease monitoring focused on the incidence, cumulative incidence, and labor performance based on the number and spatial distribution of palms detected with BR, with or without the use of the device proposed. Results showed that automatic detection using AI had low effectiveness (17.1%), identifying only a small portion of actual cases. In contrast, computer-assisted detection significantly improved accuracy, reaching 78.6% during peak months and reducing detection time by up to two months compared to traditional methods, although, its maximum performance point only reached 4.7 ha/wage. The implementation of digital monitoring provides crucial technological support by considerably improving the effectiveness of early detection in BR curative management. Future advancements in AI-based detection are expected to further improve the efficiency and functionality of this approach. Full article

The development of low-cost tools for rapid soil assessment has become a crucial field due to the increasing demands in food production and carbon storage. However, current methods for soil evaluation are costly and cannot provide enough information about the quality of samples. This work reports for the first time a low-cost 3D printed device that can be used for soil classification as well as the study of biological activity. The system incorporated multiple physical and gas sensors for the characterisation of sample types and profiling of soil volatilome. Sensing data were obtained from 31 variables, including 18 individual light wavelengths that could be used to determine seed germination rates of tomato plants. A machine learning algorithm was trained using the data obtained by characterising 75 different soil samples. The algorithm could predict seed germination rates with high accuracy (RSMLE = 0.01, and R² = 0.99), enabling an objective and non-invasive study of the impact of multiple environmental parameters in soil quality. To allow for a more complete profiling of soil biological activity, molecular imprinted-based fine particles were designed to quantify tryptophol, a quorum-sensing signalling molecule commonly used by fungal populations. This device could quantify the concentration of tryptophol down to 10 nM, offering the possibility of studying the interactions between fungi and bacterial populations. The final device could monitor the growth of microbial populations in soil, and offering an accurate assessment of quality at a low cost, impacting germination rates by incorporating hybrid data from the microsensors. Full article

Cardiovascular diseases pose a significant global health threat, with atrial fibrillation representing a critical precursor to more severe heart conditions. In this work, a multimodality-based deep learning model has been developed for diagnosing atrial fibrillation using an embedded system consisting of a Raspberry Pi 4B, an ESP8266 microcontroller, and an AD8232 single-lead ECG sensor to capture real-time ECG data. Our approach leverages a deep learning model that is capable of distinguishing atrial fibrillation from normal ECG signals. The proposed method involves real-time ECG signal acquisition and employs a multimodal model trained on the PTB-XL dataset. This model utilizes a multi-step approach combining a CNN–bidirectional LSTM for numerical ECG series tabular data and VGG16 for image-based ECG representations. A fusion layer is incorporated into the multimodal CNN-BiLSTM + VGG16 model to enhance atrial fibrillation detection, achieving state-of-the-art results with a precision of 94.07% and an F1 score of 0.94. This study demonstrates the efficacy of a multimodal approach in improving the real-time diagnosis of cardiovascular diseases. Furthermore, for edge devices, we have distilled knowledge to train a smaller student model, CNN-BiLSTM, using a larger CNN-BiLSTM model as a teacher, which achieves an accuracy of 83.21% with 0.85 s detection latency. Our work represents a significant advancement towards efficient and preventative cardiovascular health management. Full article

Hermansky–Pudlak syndrome (HPS) is a rare autosomal recessive disorder characterized by defects in lysosome-related organelles. Given the high mortality rate associated with HPS pulmonary fibrosis (PF) and the significant risks tied to lung transplantation, it is essential to explore new tools for the early surveillance of PF to monitor its progression before clinical symptoms become apparent. This study evaluates the forced oscillation technique (FOT) for assessing PF in five adult patients with HPS, all homozygous for the HPS-1 (c.1472_1487dup p.His497Glnfs*90) founder mutation. Using the Resmon™ Pro V3 device, the FOT measured resistance (Rrs) and reactance (Xrs) at 5, 11, and 19 Hertz (Hz). High-resolution computed tomography (HRCT) scans of the chest were reviewed for radiographic findings. The cohort (n = 5) had a median age of 43 years. All patients exhibited HPS clinical features, including oculocutaneous albinism and respiratory symptoms such as dry cough and dyspnea. Radiographic analysis revealed PF in four patients (80%), with traction bronchiectasis, reticular patterns, honeycombing, and ground-glass opacities. The FOT detected progressive changes in pulmonary resistance and reactance correlating with fibrosis severity. These findings suggest that the FOT is a valuable non-invasive tool for monitoring PF in patients with HPS-1, potentially improving early diagnosis and management. Full article

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6–15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14–51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements. Full article

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed. Full article

Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the interpretive description approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30–50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings. Full article

Thoracostomy and chest tube placement are key procedures in treating pleural diseases involving the accumulation of fluids (e.g., malignant effusions, serous fluid, pus, or blood) or air (pneumothorax) in the pleural cavity. Initially described by Hippocrates and refined through the centuries, chest drainage achieved a historical milestone in the 19th century with the creation of closed drainage systems to prevent the entry of air into the pleural space and reduce infection risk. The introduction of plastic materials and the Heimlich valve further revolutionized chest tube design and function. Technological advancements led to the availability of various chest tube designs (straight, angled, and pig-tail) and drainage systems, including PVC and silicone tubes with radiopaque stripes for better radiological visualization. Modern chest drainage units can incorporate smart digital systems that monitor and graphically report pleural pressure and evacuated fluid/air, improving patient outcomes. Suction application via wall systems or portable digital devices enhances drainage efficacy, although careful regulation is needed to avoid complications such as re-expansion pulmonary edema or prolonged air leak. To prevent recurrent effusion, particularly due to malignancy, pleurodesis agents can be applied through the chest tube. In cases of non-expandable lung, maintaining a long-term chest drain may be the most appropriate approach and procedures such as the placement of an indwelling pleural catheter can significantly improve quality of life. Continued innovations and rigorous training ensure that chest tube insertion remains a cornerstone of effective pleural disease management. This review provides a comprehensive overview of the historical evolution and modern advancements in pleural drainage. By addressing both current technologies and procedural outcomes, it serves as a valuable resource for healthcare professionals aiming to optimize pleural disease management and patient care. Full article

Background/Objectives: Remote monitoring (RM) cardiac implantable electronic devices for adults delivers improved patient outcomes. However, previously used bedside transmitters are not optimal due to deficient patient adherence. The goal of this study was to evaluate the efficacy of RM regarding the connectivity of smartphone app-based solutions, adherence to scheduled automatic follow-ups, and prevalence of alert-based events. Methods: We evaluated the adult heart failure (HF) population with an implanted cardiac resynchronization therapy defibrillator (CRT-D) divided into two arms: with app-based RM (abRM) and without app-based RM (control). Results: A total of 81 patients (median age of 69.0) were included in our study. Sixty-five patients received a CRT-D with abRM functionality, and sixteen did not. Twelve patients had no smartphone, and two provided no consent, resulting in their transfer to the control group. Finally, the abRM arm consisted of 51 patients, while 30 patients were in the control group. The median period of follow-up lasted 12 months. Among abRM patients, 98.0% successfully transmitted their first scheduled follow-up, and 80.4% were continuously monitored. Alert-based events were mainly related to arrhythmic events and device functionality with significantly shorter median times to notification (1 day vs. 101 days; p < 0.0001) in the abRM group. Conclusions: Our study showed a high level of compliance with timely initial transmission and adherence to scheduled remote follow-ups. Patient enrollment eligibility was a major challenge due to the limited accessibility of smartphones in the population. App-based RM demonstrated an accurate notification of events and patient-initiated transmissions in emergencies, regardless of location. Full article

The healthcare sector is undergoing a significant transformation due to the rapid expansion of data and advancements in digital technologies. The increasing complexity of healthcare data, including electronic health records (EHRs), medical imaging, and patient monitoring, underscores the necessity of big data technologies. These technologies are essential for enhancing decision-making, personalizing treatments, and optimizing operations. Digitalization further revolutionizes healthcare by improving accessibility and convenience through technologies such as EHRs, telemedicine, and wearable health devices. Cloud computing, with its scalable resources and cost efficiency, plays a crucial role in managing large-scale healthcare data and supporting remote treatment. However, integrating cloud computing in healthcare, especially in remote areas with limited network infrastructure, presents challenges. These include difficulties in accessing cloud services and concerns over data security. This article proposes a smart healthcare system utilizing the edge-cloud continuum to address these issues. The proposed system aims to enhance data accessibility and security while maintaining high prediction accuracy for disease management. The study includes foundational knowledge of relevant technologies, a detailed system architecture, experimental design, and discussions on conclusions and future research directions. Full article

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations. Full article

Transcutaneous carbon dioxide (TcPCO2) monitoring provides a non-invasive alternative to measuring arterial carbon dioxide (PaCO2), making it valuable for various applications, such as sleep diagnostics and neonatal care. However, traditional transcutaneous monitors are bulky, expensive, and pose risks such as skin burns. To address these limitations, we have introduced a compact, cost-effective CMOS imager-based sensor for TcPCO2 detection by utilizing colorimetric reactions with metal–organic framework (MOF)-based nano-hybrid materials. The sensor, with a colorimetric sensing array fabricated on an ultrathin PDMS membrane and then adhered to the CMOS imager surface, can record real-time sensing data through image processing without the need for additional optical components, which significantly reduces the sensor’s size. Our system shows impressive sensitivity and selectivity, with a low detection limit of 26 ppm, a broad detection range of 0–2% CO₂, and strong resistance to interference from common skin gases. Feasibility tests on human subjects demonstrate the potential of this MOF-CMOS imager-based colorimetric sensor for clinical applications. Additionally, its compact design and responsiveness make it suitable for sports and exercise settings, offering valuable insights into respiratory function and performance. The sensing system’s compact size, low cost, and reversible and highly sensitive TcPCO2 monitoring capability make it ideal for integration into wearable devices for remote health tracking. Full article