Health Monitoring Platform Research Articles

A Review paper on Vibration-Based Fault Diagnosis of Rolling Element Bearings

Abstract: One of the common crucial elements that decide the condition of machinery including its residual lifespan in the energy industry, automotive, hydroelectric dams, etc., is the rolling element bearing. Throughout the process, rigorous predictive condition monitoring tools like vibration control, and analysis are valuable methods that are demanded to ensure the safe condition of the rolling element bearings. The expected faults that offer ample cycle times for preventive management are suggested by a Predictive Health Forecasting instrument. Instead of simply identifying the faults, the Predictive bearing health Monitoring platform seeks to map the degradation, like wear progression. From the point of view of system condition control using vibration analysis, this article aims to summarize the fundamental science of rolling element bearings through these varied applications. Numerous vibration analysis approaches are employed to detect defects in REB. The aforementioned article explained different techniques for vibration analysis for REB defect investigation. Keywords: Frequency domain, REB, Fault Investigation, Vibration Analysis, Fault Investigation.

An Online Proactive Health Monitoring Method for Output Capacitors of Vehicular Auxiliary Converter

The auxiliary converter is the power source of the vehicular electronic system, the failure of which may cause inestimable economic loss. As the most age-affected component in the converter, the online health monitoring for output aluminum electrolytic capacitor (AEC) is of great significance. Throughout the full-life period of AEC, the capacitance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> ) will fall and the equivalent series resistance (ESR) will rise. This article proposes an online proactive health monitoring method for output capacitors of a 1-kW vehicular auxiliary converter. The operating mechanism of the two-level Boost-LLC converter is analyzed and the ESR calculation model is derived in detail. A novel ripple measurement approach and a peak current calculation method are proposed. Both ripple measurement and current approximation are confirmed to be practical and applicable. Finally, the online health monitoring platform is built. The experimental results can show the effectiveness of this method. The monitored ESR can indicate the healthy degree for the output capacitors in the auxiliary converter. The influences of switching frequency and ambient temperature are verified to express the superiority of the online monitoring scheme.

Biomarkers and Detection Platforms for Human Health and Performance Monitoring: A Review.

Human health and performance monitoring (HHPM) is imperative to provide information necessary for protecting, sustaining, evaluating, and improving personnel in various occupational sectors, such as industry, academy, sports, recreation, and military. While various commercially wearable sensors are on the market with their capability of “quantitative assessments” on human health, physical, and psychological states, their sensing is mostly based on physical traits, and thus lacks precision in HHPM. Minimally or noninvasive biomarkers detectable from the human body, such as body fluid (e.g., sweat, tear, urine, and interstitial fluid), exhaled breath, and skin surface, can provide abundant additional information to the HHPM. Detecting these biomarkers with novel or existing sensor technologies is emerging as critical human monitoring research. This review provides a broad perspective on the state of the art biosensor technologies for HHPM, including the list of biomarkers and their physiochemical/physical characteristics, fundamental sensing principles, and high‐performance sensing transducers. Further, this paper expands to the additional scope on the key technical challenges in applying the current HHPM system to the real field.

Biopolymer-based electrospun fibers in electrochemical devices: versatile platform for energy, environment, and health monitoring.

Electrochemical power tools are regarded as essential keys in a world that is becoming increasingly reliant on fossil fuels in order to meet the challenges of rapidly depleting fossil fuel supplies. Additionally, due to the industrialization of societies and the growth of diseases, the need for sensitive, reliable, inexpensive, and portable sensors and biosensors for noninvasive monitoring of human health and environmental pollution is felt more than ever before. In recent decades, electrospun fibers have emerged as promising candidates for the fabrication of highly efficient electrochemical devices, such as actuators, batteries, fuel cells, supercapacitors, and biosensors. Meanwhile, the use of synthetic polymers in the fabrication of versatile electrochemical devices has raised environmental concerns, leading to an increase in the quest for natural polymers. Natural polymers are primarily derived from microorganisms and plants. Despite the challenges of processing bio-based electrospun fibers, employing natural nanofibers in the fabrication of electrochemical devices has garnered tremendous attention in recent years. Here, various natural polymers and the strategies employed to fabricate various electrospun biopolymers are briefly covered. The recent advances and research strategies used to apply the bio-based electrospun membranes in different electrochemical devices are carefully summarized, along with the scopes in various advanced technologies. A comprehensive and critical discussion about the use of biopolymer-based electrospun fibers as the potential alternative to non-renewable ones in future technologies is briefly highlighted. This review will serve as a field opening platform for using different biopolymer-based electrospun fibers to advance the electrochemical device-based renewable and sustainable technologies, which will be of high interest to a large community. Accordingly, future studies should focus on feasible and cost-effective extraction of biopolymers from natural resources as well as fabrication of high-performance nanofibrous biopolymer-based components applicable in various electrochemical devices.

Next-generation 5G fusion-based intelligent health-monitoring platform for ethylene cracking furnace tube.

This study aimed to develop a 5G + "mixed computing" + deep learning-based next-generation intelligent health-monitoring platform for an ethylene cracking furnace tube based on 5G communication technology, with the goal of improving the health management level of the key component of ethylene production, that is, the cracking furnace tube, and focusing on the key common technical difficulties of ethylene production of tube outer-surface temperature sensing and tube slagging diagnosis. It also integrated the edge-fog-cloud "mixed computing" technology and deep learning technology in artificial intelligence, which had a higher degree in the research and development of automation and intelligence, and was more versatile in an industrial environment. The platform included a 5G-based tube intelligent temperature-measuring device, a 5G-based intelligent peep door gearing, a 5G-based edge-fog-cloud collaboration mechanism, and a mixed deep learning-related application. The platform enhanced the automation and intelligence of the enterprise, which could not only promote the quality and efficiency of the enterprise but also protect the safe operation of the cracking furnace device and lead the technological progress and transformation and upgrading of the industry through the application.

Patient-Specific Heartbeat Classification in Single-Lead ECG using Convolutional Neural Network.

For an expert cardiologist, any abnormality in the heart rhythm or electrocardiogram (ECG) shape can be easily detected as a sign of arrhythmia. However, this is a big challenge for a computer system. The need for automatic arrhythmia recognition comes from the development of many portable ECG measuring devices designed to function as a part of health monitoring platforms. These platforms, because of their wide availability, generate a lot of data and hence the need for algorithms to process this data. From the many methods for automatic heartbeat classification, convolutional neural networks (CNNs) are increasingly being applied in this ECG analysis task. The purpose of this paper is to develop arrhythmia classification model according to the standards defined by the Association for the Advancement of Medical Instruments (AAMI), using CNNs, on data from the publicly available MIT-BIH Arrhythmia database. We experiment with two types of heartbeat segmentation: static and dynamic. The ultimate goal is to implement an algorithm for long-term monitoring of a user's health, which is why we have focused on classification models from single-lead ECG, and, even more, on algorithms specifically designed for one person rather than general models. Therefore, we evaluate patient-specific CNN models also on measurements from a novel wireless single-lead ECG sensor.

ALL-071: A Phase 4 Study to Evaluate Outpatient Blinatumomab in Patients with Minimal/Measurable Residual Disease (MRD) Positivity (+) of B-Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL)

Context/Objective: Blinatumomab, a CD3/CD19-directed BiTE® (bispecific T-cell engager) molecule, is an effective treatment for patients with MRD+ BCP-ALL.1 Blinatumomab is infused continuously (cIV) 28 days/cycle. Severe adverse events (AEs), e.g., cytokine release syndrome (CRS) and neurologic toxicity (NT), may occur; thus, hospitalization is recommended for cycle 1: days 1–3 (C1:D1–3) and C2:D1–2. However, the incidence of severe AEs is low (CRS: 2%, NT: 13%).1 We believe that with digital monitoring, blinatumomab can be safely administered as an outpatient. Design: Adult patients (n=45) with BCP-ALL in complete remission and MRD+ (≥0.1% blasts) are being enrolled at ≤25 sites, with endpoint: grade ≥3 AE (Amgen NCT04506086). Patient suitability and outpatient monitoring are established. Patients receive 2–4 blinatumomab cycles. Cycles are initiated in the outpatient setting, and digital monitoring devices are activated/attached. Once home, patients set up the home hub and real-time data transfer to the healthcare provider (HCP) begins. The devices are worn continuously, 24 hours/day, for C1:D1–3 and C2:D1–2 only. Devices: Current Health’s Wearable Monitoring System (CHWMS) is an FDA-cleared platform for wireless wearable health monitoring at home. The CHWMS provides continuous oxygen saturation, respiratory rates, and heart rates; an axillary sensor provides continuous temperature. Patients manually measure blood pressure every 3–6 hours around the clock. Patients have an integrated tablet to contact the HCP if needed. HCP/designee has a smartphone and receives vital signs constantly. The CHWMS platform generates a loud alert based on pre-specified vital sign thresholds or for data transfer interruption. Regardless of whether there is an alert, HCP may initiate direct audio/video contact with the patient, assess the patient’s condition, and make appropriate interventions. Patients are required to have a caregiver present during monitoring. Patients have a full set of replacement devices and a 24/7 support hotline. Trial enrollment is underway. This study may generate feasibility data on the effectiveness of home monitoring during blinatumomab infusion in patients with MRD+ BCP-ALL. 1Gokbuget, Blood, 2018. © 2021 American Society of Clinical Oncology, Inc. Reused with permission. This abstract was accepted and previously presented at the 2021 ASCO Annual Meeting. All rights reserved.

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

Digital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.

A phase 4 study to evaluate outpatient blinatumomab in patients with minimal/measurable residual disease (MRD) positivity (+) of B-cell precursor acute lymphoblastic leukemia (BCP-ALL).

TPS7051 Background: The prognosis for adults with relapsed or refractory BCP-ALL is poor. MRD+ is the strongest predictor of relapse. Blinatumomab, a CD3/CD19-directed BiTE® (bispecific T-cell engager) molecule, is an effective treatment for patients with MRD+.1 Blinatumomab is administered as a continuous intravenous infusion (cIV) 28 days per cycle. Severe adverse events (AEs) such as cytokine release syndrome (CRS) and neurologic toxicity (NT) may occur; thus, hospitalization is recommended for the first 3 days of cycle 1 and the first 2 days of cycle 2 for MRD+ patients. However, the incidence of severe AEs is low in MRD+ BCP-ALL patients (CRS: 2%, NT: 13%).1 We believe that with the use of effective digital monitoring devices, blinatumomab can be safely administered for the entire 28-day cIV cycle as an outpatient. Methods: Adult patients (n = 45) with BCP-ALL in complete remission and MRD+ (≥0.1% blasts) are being enrolled at 25 planned treatment sites, endpoint: grade ≥3 AE during monitoring (Amgen NCT04506086). Patient suitability for blinatumomab and outpatient monitoring is established. Patients will receive 2-4 cycles of blinatumomab. Cycles are initiated in the outpatient setting, digital monitoring devices activated and attached, and patients sent home. Once home, patients set up the home hub and real-time remote data transfer to the healthcare professional (HCP) begins. The devices are worn continuously, 24 hours a day for the first 3 days of cycle 1 and the first 2 days of cycle 2 only. Devices: Current Health’s Wearable Monitoring System (CHWMS) is an FDA-cleared platform for wireless and wearable health monitoring of patients at home. The CHWMS provides continuous oxygen saturation, respiratory rate, and heart rate; an axillary temperature sensor is worn and provides continuous temperature. Patients manually measure blood pressure every 3-6 hours around the clock. Patients have an integrated mobile device (tablet) to initiate contact with the HCP if needed. HCP/designee has a mobile device (smart phone) and receives vital signs as a constant live feed transmitted from the CHWMS device. The CHWMS platform generates a loud audible alert based on pre-specified vital sign alarming thresholds or if there is an interruption in data transfer. HCP may initiate direct audio and video contact with the patient, assess the patient’s condition, and make an appropriate intervention. HCP may also initiate patient contact in the absence of an alert. Patients are required to have a caregiver present during the entire period of outpatient monitoring. Patients have a full set of replacement devices as well as a 24/7 hotline for device support. Trial enrollment is underway. This study may generate feasibility data on the effectiveness of home monitoring during blinatumomab infusion in patients with MRD+ BCP-ALL. 1Gökbuget, Blood, 2018. Clinical trial information: NCT04506086.

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project.

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink.

A36 IMPACT OF DIGITAL HEALTH MONITORING IN THE MANAGEMENT OF INFLAMMATORY BOWEL DISEASE

Abstract Background Inflammatory bowel disease (IBD) affects over 270,000 Canadians and costs the healthcare system $1.28 billion dollars annually. With advancements in technology, a shift from the traditional ‘reactive’ approach to IBD management to a ‘proactive’ approach that integrates self-management strategies using digital health monitoring platforms could greatly benefit patient care. Aims The purpose of this study was to investigate the effect of implementing the IBD health monitoring platform, HealthPROMISE, in clinical practice and to evaluate whether its use leads to better quality of care, improved health outcomes, and reduce resource consumption in patients with IBD. Methods IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/short inflammatory bowel disease questionnaire (SIBDQ) scores were flagged for their physicians to follow up with. After one-year, patient outcome metrics were compared to baseline values. Results Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p=0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p=0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p=0.48). Conclusions Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients. Future studies evaluating acceptability and costs with a larger sample size would help determine the feasibility and generalizability of widely implementing mobile health applications in the management of IBD. Funding Agencies CCC

Identifying Mobile Health Technology Experiences and Preferences of Low-Income Pregnant Women with Diabetes.

Rapid expansion of mobile technology has resulted in the development of many mobile health ("mHealth") platforms for health monitoring and support. However, applicability, desirability, and extent of tailoring of these platforms for pregnant women, particularly in populations who experience the greatest health inequities-such as women with diabetes mellitus (DM) and/or those with greater socioeconomic barriers-remains unknown. The objective is to understand low-income pregnant women's experiences and preferences for mHealth tools to support DM health and improve DM self-management during pregnancy. Low-income pregnant and postpartum women were included in individual interviews or focus groups; women with type 2 DM, gestational DM, or no DM were included. Analysis was performed with the constant comparison method. In this population of 45 (N=37 with DM) low-income, largely minority, pregnant and postpartum women, 100% reported access to smartphones and prior experience with apps. Interest in mHealth to support health and engagement during pregnancy was high. Preferences for general mHealth features included education that reduces uncertainty, support communities, visualizing progress, convenient access to information, and support for better management of pregnancy-related tasks. Preferred design elements included personalization, interactive features, and integrated graphics. Women with DM expressed multiple additional DM-specific needs, including support tools for DM self-management and self-regulation tasks. Pregnant and postpartum women, especially those with DM, desire mHealth technology to support engagement and to adapt lifestyle guidelines and treatment requirements for a healthy pregnancy. Further work to develop mHealth interventions tailored for target populations remains a key step in reducing health inequities and promoting access to evidence-based perinatal health interventions.

From Diagnosis to Treatment: Recent Advances in Patient-Friendly Biosensors and Implantable Devices.

Patient-friendly medical diagnostics and treatments have been receiving a great deal of interest due to their rapid and cost-effective health care applications with minimized risk of infection, which has the potential to replace conventional hospital-based medical procedures. In particular, the integration of recently developed materials into health care devices allows the rapid development of point-of-care (POC) sensing platforms and implantable devices with special functionalities. In this review, the recent advances in biosensors for patient-friendly diagnosis and implantable devices for patient-friendly treatment are discussed. Comprehensive analysis of portable and wearable biosensing platforms for patient-friendly health monitoring and disease diagnosis is provided, including topics such as materials selection, device structure and integration, and biomarker detection strategies. Moreover, specific challenges related to each biological fluid for wearable biosensor-based POC applications are presented. Also, advances in implantable devices, including recent materials development and wireless communication strategies, are discussed. Furthermore, various patient-friendly surgical and treatment approaches are reviewed, such as minimally invasive insertion and mounting, in vivo electrical and optical modulations, and post-operation health monitoring. Finally, the challenges and future perspectives toward the development of the patient-friendly diagnosis and treatment are provided.

Impact of Digital Health Monitoring in the Management of Inflammatory Bowel Disease.

Technological advances now permit self-management strategies using mobile applications which could greatly benefit patient care. The purpose of this study was to investigate whether the use of the inflammatory bowel disease (IBD) digital health monitoring platform, HealthPROMISE, leads to better quality of care and improved health outcomes in IBD patients. IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/SIBDQ scores were flagged for their physicians to follow up. After one-year, patient outcome metrics were compared to baseline values. Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p = 0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p = 0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p = 0.48). Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10916-021-01706-x.

Long-term In Vivo Monitoring of Chemicals with Fiber Sensors

Long-term in vivo monitoring of chemicals with implanted sensors has received considerable interests over the past decades owing to their significant contributions in reflecting health conditions and assistance in diagnosing diseases. However, the widely explored chemical sensors outside the body fail to meet the requirements of in vivo applications. This perspective reviews main challenges, recent advances and future directions of long-term in vivo monitoring of chemicals, related to immune response and sensing performance. Challenges in terms of the immune response caused by unstable interfaces between sensors and tissues and improper implanting methods, and the insufficient performance of chemical sensors in complex physiological environment are discussed. Therewith, recent advances in fabricating biocompatible, flexible and thin sensors, developing effective implanting methods with reduced injury and improving the sensitivity, selectivity and stability of chemical sensors for accurate monitoring in vivo are summarized. Finally, we propose the future directions to address these challenges by fiber chemical sensors through the combination of soft fiber configuration, facile implanting methods and new recognition elements, which will provide new platforms for health monitoring and physiological mechanism revealing.

Inkjet printing for flexible and wearable electronics

Flexible and wearable electronic devices are emerging as the novel platform for portable health monitoring, human–machine interaction, and some other electronic/optic applications. Future development of human-friendly smart electronics relies on efficient manufacturing and processing of advanced functional materials on flexible/stretchable substrates with effective device integration. Inkjet printing, known as a highly efficient solution-based printing and patterning technology with low-cost, high-quality, and high-throughput advantages, suits large-scale fabrication of flexible and wearable electronics. Over the years, researchers focused on high pattern resolution and uniformity on flexible substrates for advanced electrical/optical performances by various inkjet printing techniques. Different ink materials that can realize multiple functions have been fully investigated for achieving favorable printability and desired interactions with the substrates. Here, the most recently reported inkjet printing strategies, functional ink materials, and diverse inkjet-printed wearable electronic devices for practical applications (e.g., sensors, displays, transistors, and energy storage devices) are summarized. An outlook on future challenges as well as opportunities of inkjet-printed flexible and wearable electronics for research development and industrial commercialization is also presented.

Global challenges in cognitive assessment: How technology will drive neuropsychologists to the forefront

AbstractBackgroundAge remains the strongest risk factor for Alzheimer’s disease (AD). With the number of people over 65 now outnumbering those under the age of 5, the global burden of AD is rapidly increasing. Cognitive decline is the hallmark behavioral symptom of AD. However, accurate cognitive assessment requires consideration of a myriad of factors, including education, language, and culture. In fact, in a majority of low‐to‐middle income countries, as well as many emerging economies, there is no formalized doctoral training in neuropsychology. These circumstances necessitate embracing novel data collection methods in order to preserve the voice of the neuropsychologist. Technological advances can provide the tools to address the global realities of AD and other related disorders.MethodWe have developed a brain health monitoring platform that leverages the relatively ubiquitous use of smartphones across virtually all countries and collects behavioral measures that are inextricably linked to cognitive skills. We are applying machine learning approaches to quantify digital indices into cognitive ones. Importantly, we have strategically selected mobile applications that measure behaviors that are 1) common regardless of age, education, language and culture and 2) can be flexibly customized to circumstances.ResultWe have begun deploying versions of this high tech‐driven platform in the U.S. Further, we are starting to have an impact internationally, particularly in Asia. The health and age demographics in China make its population particularly vulnerable to AD. We present findings that speak to this risk and how our platform, and others like it, will help fill the neuropsychological expertise gap that such countries are currently experiencing.ConclusionAD is a rising epidemic. Early and accurate detection is the first step to finding disease modifying interventions and providing the context necessary for maximized efficacy. The coupling of professional neuropsychology skills and digital technology will generate solutions that can sufficiently address the impending challenge of conducting cognitive assessments universally. This potentially includes resolving longstanding health disparities by extending neuropsychologists’ reach into regions with significant economic and/or geographical constraints.

Colorimetric Detection Based on Localized Surface Plasmon Resonance for Determination of Chemicals in Urine.

Colorimetric sensors based on localized surface plasmon resonance (LSPR) have attracted much attention for biosensor and chemical sensor applications. The unique optical effect of LSPR is based on the nanostructure of noble metals (e.g., Au, Ag, and Al) and the refractive index of the environment surrounding these metal nanomaterials. When either the structure or the environment of these nanomaterials is changed, their optical properties change and can be observed by spectroscopic techniques or the naked eye. Colorimetric-probe-based LSPR provides a simple, rapid, real-time, nonlabelled, sensitive biochemical detection and can be used for point-of-care testing as well as rapid screening for the diagnosis of various diseases. Gold and silver nanoparticles, which are the two most widely used plasmonic nanomaterials, demonstrate strong and sensitive LSPR signals that can be used for the selective detection of several chemicals in biochemical compounds provided by the human body (e.g., urine and blood). This information can be used for the diagnosis of several human health conditions. This paper provides information regarding colorimetric probes based on LSPR for the detection of three major chemicals in human urine: creatinine, albumin, and glucose. In addition, the mechanisms of selective detection and quantitative analysis of these chemicals using metal nanoparticles are discussed along with colorimetric-detection-based LSPR for many other specific chemicals that can be detected in urine, such as catecholamine neurotransmitters, thymine, and various medicines. Furthermore, issues regarding the use of portable platforms for health monitoring with colorimetric detection based on LSPR are discussed.

Acceptance and Potential Impact of the eWALL Platform for Health Monitoring and Promotion in Persons with a Chronic Disease or Age-Related Impairment.

Pervasive health technologies can increase the effectiveness of personal health monitoring and training, but more user studies are necessary to understand the interest for these technologies, and how they should be designed and implemented. In the present study, we evaluated eWALL, a user-centered pervasive health technology consisting of a platform that monitors users’ physical and cognitive behavior, providing feedback and motivation via an easy-to-use, touch-based user interface. The eWALL was placed for one month in the home of 48 subjects with a chronic condition (chronic obstructive pulmonary disease—COPD or mild cognitive impairment—MCI) or with an age-related impairment. User acceptance, platform use, and potential clinical effects were evaluated using surveys, data logs, and clinical scales. Although some features of the platform need to be improved before reaching technical maturity and making a difference in patients’ lives, the real-life evaluation of eWALL has shown how some features may influence patients’ intention to use this promising technology. Furthermore, this study made it clear how the free use of different health apps is modulated by the real needs of the patient and by their usefulness in the context of the patient’s clinical status.

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.