Health Monitoring Platform 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.

Bridge monitoring using mobile sensing data with traditional system identification techniques

AbstractMobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

Virtual reality visualisation of automatic crack detection for bridge inspection from 3D digital twin generated by UAV photogrammetry

This research develops a UAV photogrammetry methodology to generate 3D digital twins (DT) of the Elvanlı Bridge in Mersin, Turkey. These DTs are then analyzed using advanced image processing algorithms for automatic crack detection. The identified cracks were incorporated into the 3D model, creating a DT to damage-augmented digital twins (DADT). Subsequently, the DADT was rendered in virtual reality, offering an interactive alternative solution with remote access to on-site structural visual inspections in a digital environment. This approach has increased the objectivity and efficiency of inspections in the digital environment. The results demonstrate the potential of this integrated approach to enhance the accuracy and reliability of bridge inspections, reduce operational costs, and provide a comprehensive platform for structural health monitoring through non-invasive methods. The study transformative impact of combining emerging technologies such as UAV photogrammetry and VR in civil engineering, paving the way for more systematic and precise infrastructure assessments.

Railways Health Monitoring Employing KSK Approach: A Novel AIIoT based Decision-Making Approach for Railways

Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.

Acceptability, feasibility, and impact of the MyGut digital health platform in the monitoring and management of inflammatory bowel disease

Abstract Background Digital health monitoring may help facilitate self-management strategies when caring for patients with inflammatory bowel disease (IBD). Aims This study investigated the feasibility of implementing the MyGut health application when caring for patients with IBD and evaluated whether its use improved health outcomes. Methods We conducted a prospective trial in 2 Canadian hospitals from 2020 to 2023. Patients with IBD were recruited from gastroenterology clinics, and the MyGut application was installed onto their mobile devices. Metrics such as acceptability, satisfaction, feasibility, quality-of-life scores (measured through the short IBD questionnaire [SIBDQ]), and resource utilization were collected throughout the 1-year follow-up period. Results Of the 84 patients enrolled, 58 patients (69%) continued to use the app until the study completion. At recruitment, all 84 patients (100%) were willing to use the MyGut application after a brief tutorial. There was a significant improvement in the SIBDQ scores after 1 year of MyGut use (mean = 56.0, SD 8.85 vs 52.0, SD 9.84) (P = .012). However, only 42.9% (21/49) of the patients were willing to continue using the application after 1 year, a significant decrease compared with the 71.4% (35/49) who were willing to continue after 2 months (P = .001). No differences were observed in the number of emergency room visits/hospitalizations (P = .78) before and after 1 year of MyGut use. Conclusions This study demonstrates that patients are willing to use digital health monitoring platforms and this may lead to improved quality of life. However, sustained efforts must be made to optimize its long-term feasibility.

Recent Progress on Flexible Self-Powered Tactile Sensing Platforms for Health Monitoring and Robotics.

Over the past decades, tactile sensing technology has made significant advances in the fields of health monitoring and robotics. Compared to conventional sensors, self-powered tactile sensors do not require an external power source to drive, which makes the entire system more flexible and lightweight. Therefore, they are excellent candidates for mimicking the tactile perception functions for wearable health monitoring and ideal electronic skin (e-skin) for intelligent robots. Herein, the working principles, materials, and device fabrication strategies of various self-powered tactile sensing platforms are introduced first. Then their applications in health monitoring and robotics are presented. Finally, the future prospects of self-powered tactile sensing systems are discussed.

A comparative study on humidity response of graphene oxide with reduced graphene oxide and its multifunctional applications

In this paper, we present a comparative study of Graphene Oxide (GO) and chemically reduced Graphene Oxide (rGO) based resistive type humidity sensors fabricated on Ti/Au Inter-Digital Electrodes (IDEs) coated SiO2 substrates. The sensing performance, including sensitivity, response/recovery time, and hysteresis has been evaluated for both sensors. The GO film exhibited quick response (0.3 s) and high sensitivity (104.7 kΩ/%RH) with remarkable repeatability and negligible hysteresis as compared to rGO film sensor. The excellent humidity sensing properties of GO are attributed to the presence of abundant oxygen-containing polar functional groups on GO relative to rGO. To demonstrate the multifunctional applications of developed GO based sensor, we have also demonstrated its use as respiratory monitoring, non-contact proximity detector, and speech event detector through the detection of humidity perturbations. The work suggests GO sensitive material has high potential for the development of wearable and integrated health monitoring platforms and non-contact sensing devices.

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.

Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments. This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults. A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability. Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18). The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability. ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

AIE-based fluorescent sensing of ciprofloxacin using non-fluorescent 2D silver dimolybdate nanosheets: The synergistic sensor method (SGSM) to switch on non-fluorescent 2D nanosheets into an effective fluorescent biosensor

The development of an efficient method for detecting ciprofloxacin (CIP) is critical due to the potential for significant harm to humans, including the danger of severe liver damage and hematuria associated with increased levels of this drug. In this study, we offer a highly sensitive and highly selective method for fluorescence sensing of the antibiotic ciprofloxacin employing the properties of Aggregation-Induced Emission (AIE) with silver dimolybdate nanosheets (Ag2Mo2O7 NSs). Outstanding structural characteristics of the Ag2Mo2O7 NSs achieved using the hydrothermal method. By carefully controlling the aggregation of the Synergistic Sensor Method (SGSM), we are able to produce a very bright fluorescence response with the ciprofloxacin concentration range from 0 to 250 nM with a low detection limit of 1.1 nM. Quick and ultrasensitive detection has been achieved by the Synergistic effect (SGE) and AIE from the current approach. The SGSM has been shown to be a novel platform for human health monitoring and pharmaceutical quality control. With broad-ranging practical implications, the combination of AIE and silver dimolybdate nanosheets offers a platform for reliable and ciprofloxacin detection. We believe the SGSM is a role model paper for future nano-biosensor detection methods due to the synergistic effect resulting in the AIE and turn-on sensor approach for many non-fluorescent 2D nanosheets. This can open many new doors for the intensive development of 2D nanosheets for future fluorescent sensors without the need to produce fluorescence by the nanosheet itself.

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Feasibility and Acceptability of a Mobile App for Prolonged Grief Disorder Symptoms.

Mobile apps provide a unique platform for mental health assessment and monitoring. They can provide real time, accessible data on symptoms of mental disorders that may yield rich data for detailed clinical assessment and help individuals gain insight into their current mental state. We developed one of the first apps for tracking symptoms of prolonged grief disorder. In this pilot feasibility study, we assess the feasibility and acceptability of a new mobile app mGAGE for use once a day for 3 weeks. 27 participants completed mental health assessments at t1 and t2. Adherence to the app protocol was very high with 100% for the first two weeks of use. A surprising finding was the improvement of grief symptoms at t2. Debriefing interviews revealed general qualitative categories including positive feedback, negative feedback and specific recommendations. Overall, the app was found to be feasible for use for the first two weeks and acceptable for bereaved individuals. This app could provide valuable data for in depth clinical assessment, may support individuals to gain greater insight into their symptoms and may have a therapeutic effect in terms of improved grief symptoms. Implications for future studies including use in larger intervention studies are discussed.

A278 ACCEPTABILITY, FEASIBILITY, AND IMPACT OF THE MYGUT DIGITAL HEALTH PLATFORM IN THE MONITORING AND MANAGEMENT OF INFLAMMATORY BOWEL DISEASE

Abstract Background Inflammatory bowel disease (IBD) is a chronic gastrointestinal disorder that has been associated with depression, anxiety, and decreased quality of life. With new technological advancements, digital health monitoring platforms offer a promising avenue to integrate self-management strategies into the care of patients with IBD. Aims This study aimed to investigate patients’ acceptability to implementing the MyGut IBD health monitoring platform into their care, evaluate whether its use leads to better quality of life/improved health outcomes, and determine the feasibility of its long-term use. Methods We conducted a multi-centre, single-arm trial at McMaster University and McGill University, among their affiliated hospitals, from September 2020 to September 2023. Patients with IBD were recruited in gastroenterology clinics and asked to install the MyGut application onto their mobile devices. Various metrics such as willingness to use the application, patient satisfaction, symptom control/quality of life scores (measured through the short inflammatory bowel disease questionnaire (SIBDQ)), resource utilization, and feasibility statements were collected throughout the study. Patients exhibiting abnormal SIBDQ scores were identified for follow-up. After one year, patient outcome metrics were compared to baseline values. Results Out of the 84 patients initially enrolled, 58 patients (69%) continued until study completion at one-year. At recruitment, all 84 patients (100%) were willing to use the MyGut application after a brief tutorial and 72.6% (61/84) had already been using technology for health-related purposes. There was a significant improvement in SIBDQ scores after one year of MyGut use (median = 57.5, IQR 50.25–62) when compared to baseline (median = 54.5, IQR 47.25–58.75) (p = 0.01). However, only 42.9% of patients (21/49) were willing to continue using the application after one year, a significant decrease compared to the 71.9% (41/57) who were willing to continue after two months (p = 0.002). No differences were observed in patient satisfaction measures (p = 0.42 to 1) or number of ER visits/hospitalizations (p = 0.78) before and after one year of MyGut use. Conclusions Overall, IBD is a condition that often necessitates frequent medical follow-up and may benefit from proactive symptom management strategies. This study demonstrates that patients with IBD are very willing to integrate digital health monitoring platforms into their care and these may subsequently lead to improved symptom control and enhanced quality of life. Future studies should aim to investigate the subset of patients that would benefit most from these health interventions, and as illustrated, continued efforts must be made to optimize the acceptability/feasibility of its long-term use. Funding Agencies CCC

3D Printable Hydrogel Bioelectronic Interfaces for Healthcare Monitoring and Disease Diagnosis: Materials, Design Strategies, and Applications

AbstractIn recent years, additive manufacturing tools, such as 3D printing, has gained enormous attention in biomedical engineering for developing ionotropic devices, flexible electronics, skin‐electronic interfaces, and wearable sensors with extremely high precision and sensing accuracy. Such printed bioelectronics are innovative and can be used as multi‐stimuli response platforms for human health monitoring and disease diagnosis. This review systematically discusses the past, present, and future of the various printable and stretchable soft bioelectronics for precision medicine. The potential of various naturally and chemically derived conductive biopolymer inks and their nanocomposites with tunable physico‐chemical properties is also highlighted, which is crucial for bioelectronics fabrication. Then, the design strategies of various printable sensors for human body sensing are summarized. In conclusion, the perspectives on the future advanced bioelectronics are described, which will be helpful, particularly in the field of nano/biomedicine. An in‐depth knowledge of materials design to functional aspects of printable bioelectronics is demonstrated, with an aim to accelerate the development of next‐generation wearables.

IoT platform for offshore wind turbine blade structure health monitoring

Wind energy, as renewable energy, is critical in targeting carbon neutrality or net zero emissions in order to address global climate change. Compared with onshore wind turbines, offshore wind turbines enjoy generally higher wind speed, thus producing more electric energy. However, the harsh marine environment, including high winds, wave-induced vibrations and and sea and rain corrosion and erosion, can lead to structural damage, reduced operational efficiency and increased maintenance cost. This paper presents a novel Internet of Things (IoT) platform for structural health monitoring (SHM) of the offshore wind turbine’s key component, the wind turbine blades. This research focuses on developing a comprehensive, real-time monitoring system that utilises advanced sensor networks, edge computing, and advanced predictive algorithms to strengthen on-time maintenance of turbine blades, while avoiding unnecessary and costly regular maintenance.

IoT and AI in Livestock Management: A Game Changer for Farmers

This review article explores the transformative impact of AI and IoT in livestock management, highlighting their pivotal role in advancing Agriculture 4.0. It delves into various technologies such as robotics, nanotechnology, and gene editing, which are reshaping farming and food systems towards sustainability. The paper emphasizes the significance of digital phenotyping in poultry, particularly in enhancing productivity, animal welfare, and sustainability through innovative genomics research and health monitoring platforms. Additionally, it examines the evolution of e-agriculture in India, focusing on government initiatives and the increasing influence of mobile technology in farming. The role of Big Data in Smart Farming is also scrutinized, revealing its extensive impact beyond primary production and its potential in reshaping supply chain dynamics and business models. The review further assesses the contributions of IoT in agricultural systems, particularly in meeting the challenges of a rapidly growing global population. Through this comprehensive analysis, the paper underscores the necessity for ongoing research and development in these areas, recognizing the potential challenges and opportunities presented by these robust, sustainable, and in creating a more technologically advanced agricultural future.

Emerging Additive Manufacturing Methods for Wearable Sensors: Opportunities to Expand Access to Personalized Health Monitoring

AbstractPersistent disparities exist in access to state‐of‐the‐art healthcare disproportionately affecting underserved and vulnerable populations. Advances in wearable sensors enabled by additive manufacturing (AM) offer new opportunities to address such disparities and enhance equitable access advanced diagnostic technologies. Additive manufacturing provides a pathway to rapidly prototype bespoke, multifunctional wearable sensors thereby circumventing existing barriers to innovation for resource‐limited settings imposed by the need for specialized facilities, technical expertise, and capital‐intensive processes. This review examines recent progress in the additive manufacture of wearable platforms for physiological health monitoring. Supported by an initial overview of relevant techniques, representative examples of 3D printed wearable sensors highlight the potential for measuring clinically‐relevant biophysical and biochemical signals of interest. The review concludes with a discussion of the promise and utility of additive manufacturing for wearable sensors, emphasizing opportunities for expanding access to vital healthcare technology and addressing critical health disparities.

Cloud-Based Platforms for Health Monitoring: A Review

Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit.