Search Results (511)

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets. Full article

In recent years, with the increased interest in smart home technology and the increased need to remotely monitor patients due to the pandemic, demand for contactless systems for vital sign measurements has also been on the rise. One of these kinds of systems are Doppler radar systems. Their design is composed of several choices that could possibly have a significant impact on their overall performance, more specifically those focused on the measurement of cardiac activity. This review, conducted using works obtained from relevant scientific databases, aims to understand the impact of these design choices on the performance of systems measuring either heart rate (HR) or heart rate variability (HRV). To that end, an analysis of the performance based on hardware architecture, carrier frequency, and measurement distance was conducted for works focusing on both of the aforementioned cardiac parameters, and signal processing trends were discussed. What was found was that the system architecture and signal processing algorithms had the most impact on the performance, with FMCW being the best performing architecture, whereas factors like carrier frequency did not have an impact.This means that newer systems can focus on cheaper, lower-frequency systems without any performance degradation, which will make research easier. Full article

In medical healthcare services, Wireless Body Area Networks (WBANs) are enabler tools for tracking healthcare conditions by monitoring some critical vital signs of the human body. Healthcare providers and consultants use such collected data to assess the status of patients in intensive care units (ICU) at hospitals or elderly care facilities. However, the collected data are subject to anomalies caused by faulty sensor readings, malicious attacks, or severe health degradation situations that healthcare professionals should investigate further. As a result, anomaly detection plays a crucial role in maintaining data quality across various real-world applications, including healthcare, where it is vital for the early detection of abnormal health conditions. Numerous techniques for anomaly detection have been proposed in the literature, employing methods like statistical analysis and machine learning to identify anomalies in WBANs. However, the lack of normal datasets makes training supervised machine learning models difficult, highlighting the need for unsupervised approaches. In this paper, a novel, efficient, and effective unsupervised anomaly detection model for WBANs is developed using the autoencoder convolutional neural network (CNN) technique. Due to their ability to reconstruct data in a completely unsupervised manner using reconstruction error, autoencoders hold great potential. Real-world physiological data from the PhysioNet dataset evaluated the suggested model’s performance. The experimental findings demonstrate the model’s efficacy, which provides high detection accuracy, as reported F1-Score is 0.96 with a batch size of 256 along with a mean squared logarithmic error (MSLE) below 0.002. Compared to existing unsupervised models, the proposed model outperforms them in effectiveness and efficiency. Full article

Currently, wearable sensors can measure vital sign frequencies, such as respiration rate, but they fall short of providing quantitative data, such as respiratory tidal volume. Meanwhile, the airflow at the mouth carries both the frequency and quantitative respiratory signals. In this study, we propose a method to calibrate a wearable piezoelectric thread sensor placed on the chest using mouth airflow for accurate quantitative respiration monitoring. Prior to human trials, we introduced an artificial ventilator as a test subject. To validate the proposed concept, we embedded a miniaturized tube airflow sensor at the ventilator’s outlet, which simulates human respiration, and attached a wearable piezoelectric thread to the piston, which moves periodically to mimic human chest movement. The integrated output readings from the wearable sensor aligned with the airflow rate measurements, demonstrating its ability to accurately monitor not only respiration rate but also quantitative metrics such as respiratory volume. Finally, tidal volume measurement was demonstrated using the wearable piezoelectric thread. Full article

Stress and dermatosis or itching are strictly related to mange caused by Sarcoptes spp. and Chorioptes spp. mites, which are particularly stressful in alpacas. Treatment is challenging due to limited options, poor response, and low topical efficacy. Paradoxically, veterinary procedures can exacerbate stress, worsening the clinical conditions of these fragile prey species. This case report aims to highlight the key role of stress in the onset and evolution of dermatological diseases. Three alpacas, introduced to an animal theme park, developed dermatological issues three months later. Clinical exams revealed mild itching, and alopecic and hyperkeratotic lesions on their ears, belly, and limbs. Skin scrapings confirmed Sarcoptes scabiei and Chorioptes bovis. Subcutaneous ivermectin was administered at 0.2 mg/kg, repeated at 7, 14, and 21 days. Weekly inspections monitored disease progression and stress responses, including increased vigilance, running, jumping, vocalizations, tachycardia, and tachypnoea. Due to the persistent stress and despite positive parasitological test results, treatment was suspended after day 21, with remote disease monitoring. A month later, the parasitological exam was negative, and three months later, all alpacas showed no clinical signs of complete hair regrowth. Understanding and addressing stress in alpacas is vital in veterinary practice for effective disease management and overall well-being. Full article

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications. Full article

Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer. Full article

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system’s reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts. Full article

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement. Full article

Background: Despite the implementation of enhanced recovery programs, the reported average postoperative length of stay after robotic lobectomy remains as 4 days. In this prospective study, we present the outcomes of early discharge (on day 2) with telehealth home monitoring device after robotic lobectomy for lung cancer in selected patients. Methods: All patients with a caregiver were discharged on postoperative day 2 (POD 2) with a telemonitoring device provided they met the specific discharge criteria. Inclusion criteria: <75 years old, stage I-II NSCLC, with caregiver, ECOG 0–2, scheduled for lobectomy, logistic proximity to hospital (<60 km); intra-postoperative exclusion criteria: conversion to open surgery, early complications needing hospital monitoring or redo-operation, difficult pain management, <92 HbO2% saturation on room air or need for O2 supplementation, altered vital or laboratory parameters. Teleconsultations were scheduled as follows: the first one in afternoon of POD2, two on POD3, then once a day until chest tube removal. After discharge, patients recorded their vital signs at least four times a day using the device, which allowed two surgeons to monitor them via a mobile application. In the event of sudden changes in vital signs or the occurrence of adverse events, patients had access to a direct phone line and a dedicated re-hospitalization pathway. The primary outcome was safety, assessed by the occurrence of post-discharge complications or readmissions, as well as feasibility. Secondary outcomes: comparison of safety profile with a matched control group in which the standard of care and the evaluation of resource optimization were maintained and economic evaluation. Results: Between July 2022 and February 2024, 48 patients were enrolled in the present study. Six patients (12.5%) dropped out due to unsatisfied discharge criteria on POD2. Exclusion causes were: significant air leaks (n:2) requiring monitoring and the use of suction device, uncontrolled pain (n:2), atrial fibrillation, and occurrence of cerebral ischemia (n:1 each). The adherence rate to vital signs monitoring by patients was 100%. A mean number of four measurements per day was performed by each patient. During telehealth home monitoring, a total of 71/2163 (1.4%) vital sign measurements violated the established acceptable threshold in 22 (52%) patients. All critical violations were managed at home. During the surveillance period (defined as the time from POD 2 to the day of chest tube removal), a persistent air leak was recorded in one patient requiring readmission to the hospital (on POD 13) and re-intervention with placement of a second thoracic drainage due to unsatisfactory lung expansion. No other postoperative complication occurred nor was there any readmission needed. Compared to the control group, the discharge gain was 2.5 days, with an economic benefit of 528 €/day (55.440 € on the total enrolled population). Conclusions: Our results confirm that the adoption of telehealth home monitoring is feasible and allows a safe discharge on postoperative day two after robotic surgery for stage I-II NSCLC in selected patients. A potential economic benefit (141 days of hospitalizations avoided) for the healthcare system could result from the adoption of this protocol. Full article

Over the past decade, wearable medical devices (WMDs) have become the norm for continuous health monitoring, enabling real-time vital sign analysis and preventive healthcare. These battery-powered devices face computational power, size, and energy resource constraints. Traditionally, low-power microcontrollers (MCUs) and application-specific integrated circuits (ASICs) have been used for their energy efficiency. However, the increasing demand for multi-modal sensors and artificial intelligence (AI) requires more computational power than MCUs, and rapidly evolving AI asks for more flexibility, which ASICs lack. Field-programmable gate arrays (FPGAs), which are more efficient than MCUs and more flexible than ASICs, offer a potential solution when optimized for energy consumption. By combining real-time reconfigurability with intelligent energy optimization strategies, FPGAs can provide energy-efficient solutions for handling multimodal sensors and evolving AI requirements. This paper reviews low-power strategies toward FPGA-based WMD for physiological monitoring. It examines low-power FPGA families, highlighting their potential in power-sensitive applications. Future research directions are suggested, including exploring underutilized optimizations like sleep mode, voltage scaling, partial reconfiguration, and compressed learning and investigating underexplored flash and hybrid-based FPGAs. Overall, it provides guidelines for designing energy-efficient FPGA-based WMDs. Full article

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance. Full article

Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities. Full article

Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning. Full article

Background/Objectives: Early identification of complications in chronic and infectious diseases can reduce clinical deterioration, lead to early therapeutic interventions and lower morbidity and mortality rates. Here, we aimed to assess the feasibility of a novel clinical decision support system (CDSS) based on the automatic generation of alerts through remote patient monitoring and to identify the patient profile associated with the likelihood of severe medical alerts. Methods: A prospective, multicenter, open-label, randomized controlled trial was conducted. Patients with COVID-19 in home isolation were randomly assigned in a 1:1 ratio to receive either conventional primary care telephone follow-up plus access to a mobile app for self-reporting of symptoms (control group) or conventional primary care telephone follow-up plus access to the mobile app for self-reporting of symptoms and wearable devices for real-time telemonitoring of vital signs (case group). Results: A total of 342 patients were randomized, of whom 247 were included in the per-protocol analysis (103 cases and 144 controls). The case group received a more exhaustive follow-up, with a higher number of alerts (61,827 vs. 1825; p < 0.05) but without overloading healthcare professionals thanks to automatic alert management through artificial intelligence. Baseline factors independently associated with the likelihood of a severe alert were having asthma (OR: 1.74, 95% CI: 1.22–2.48, p = 0.002) and taking corticosteroids (OR: 2.28, 95% CI: 1.24–4.2, p = 0.008). Conclusions: The CDSS could be successfully implemented and enabled real-time telemonitoring of patients’ clinical status, providing valuable information to physicians and public health agencies. Full article