Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Melillo, Paolo; Orrico, Ada; Scala, Paolo; Crispino, F.; Pecchia, Leandro

doi:10.1007/s10916-015-0294-3

Cited by 58 publications

(16 citation statements)

References 40 publications

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“…The symbol () indicates that the research paper uses the checked technology and the opposite is indicated by the symbol (). [15] Chronic diseases [2] Cardiovascular [26] Heart diseases [152] Ubiquitous monitoring system [28] Pain assessment [29] Heart diseases [40] Knees rehabilitation [153] Vital signs gathering and processing [46] Chronic diseases [47] Hypertension [57] Tracking daily activities [61] EXP carried on healthy volunteers [92] Context aware monitoring [77] Diabetes and Diet monitoring [96] Heart diseases [97] Diabetes [90] Diabetes [93] Mental disorder [154] Chronic diseases [155] Monitor patients with depression [131] Cardiovascular diseases [156] Hypertension, hypotension [157] Diabetes [158] Heart diseases [159] Knee arthroplasty [160] Elderly [161] Diabetes [162] Parkinson's disease [106] Fall detection [117] Diabetes [116] Alzheimer's Currently, many health monitoring projects and applications have been initiated that use different architectures. Health monitoring systems are heterogeneous and have been developed for various diseases and disabilities.…”

Section: Study Resultsmentioning

confidence: 99%

“…This system gathered all the vital signs sensed by biosensors and automatically uploaded them to a cloud service (Xively), permitting physicians and other caregivers to remotely monitor patients in real-time. The same idea is in [ 47 ], where the authors provided a monitoring system for heart diseases. This system provided real-time monitoring and predicted future risk over the next 12 months using data mining techniques.…”

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

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Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

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Section: Study Resultsmentioning

confidence: 99%

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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“…In CDDS envisioned end-users are not only experienced cardiologists, but also patients, caregivers, general practitioners, nurses, trainees, and so forth. Moreover, once trained, the network responds in a very short time and can be embedded into mobile phones or deployed to cloud architectures, as we already discussed elsewhere [13,55].…”

Section: Discussionmentioning

confidence: 99%

A convolutional neural network approach to detect congestive heart failure

Porumb

Iadanza

Massaro

et al. 2020

Biomedical Signal Processing and Control

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Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG's morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners' needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.

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“…In particular, such a system provides a feasible solution to the increasing demand for long-term care support and monitoring to community-dwelling elderly individuals who may have difficulties in accessing health services [ 13 - 16 ]. A review of the current literature showed that telehealth systems have been assisting older adults in specific health-related areas such as chronic conditions [ 17 - 22 ], falls [ 23 , 24 ], and general wellness [ 25 - 28 ]. For example, Or and Tao [ 19 ] developed a patient-centered, tablet computer-based self-monitoring system to enable older adults with type 2 diabetes and hypertension to measure and monitor their blood glucose and blood pressure.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Wang¹,

Zhao²,

Yu³

et al. 2020

J Med Internet Res

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Background Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

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Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Cited by 58 publications

References 40 publications

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

A convolutional neural network approach to detect congestive heart failure

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

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