Eye on Patient Care: Continuous Health Monitoring: Design and Implementation of a Wireless Platform for Healthcare Applications

Miranda, Jorge; Memon, Mukhtiar; Cabral, Jorge; Ravelo, Blaise; Wagner, Stefan; Pedersen, Christian Fischer; Mathiesen, Morten; Nielsen, Christina

doi:10.1109/mmm.2016.2635898

Cited by 17 publications

(9 citation statements)

References 28 publications

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“…As this application space matures a clear need for spectral management will arise to support many sensors [70]. Spectral management for wireless sensors would enable multiple patients to be monitored for critical activity via remote sensing [71]. Feasibility of such sensors has been demonstrated through patient fall detection [3], health monitoring using through-the-wall UWB radars for heart and breathing rates [72], and even the identification of abnormalities in the detected heart and breathing rates such as heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) [73].…”

Section: Discussionmentioning

confidence: 99%

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Beal

Cohen

Syed

2020

Sensors

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High entropy waveforms exhibit desirable correlation properties in radar and sonar applications when multiple systems are used in close proximity. Unfortunately, the information content of these signals can impose high sampling requirements for digital detection techniques. Solvable chaotic oscillators have been proposed to address such issues due to their simple, matched filters, where hardware has been demonstrated with a bandwidth of 10–20 kHz. To extend applications of these systems, we present theory, design, and experimental verification of solvable chaos at 1 MHz using simple off-the-shelf components. The waveforms produced by this system were propagated over a 2.45 GHz RF link and detected with an RLC-based, purely analog matched filter. Further, we show that properties of this special class of chaotic systems can be exploited to yield RF noise sources that are generally advantageous for multi-user ranging applications when compared to conventional techniques. The result is a simple, low-cost, and potentially low-power RF ranging system that requires very little digital signal processing.

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Section: Discussionmentioning

confidence: 99%

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Beal

Cohen

Syed

2020

Sensors

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“…To meet public and industrial needs, electronics and communication engineering are moving toward smart technologies, such as smart devices for healthcare, smart cities, smart homes, and smart cars [1][2][3][4]. In all aspects, wireless communication is required with different conformal structures [5,6].…”

Section: Introductionmentioning

confidence: 99%

Systematic Flexible Antenna Performance Study of V-folding Percentage Influence

Ayyala¹,

Thakur²,

Baccar³

et al. 2023

PIER C

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In wireless technology, microstrip patch antennas are often used in communication systems with various designs. However, the effect of geometrically folded antennas on wireless communication performance is unclear. To address this problem, an in-depth study of the flexible antenna parameters was performed through V-folding analysis. A systematic and complete analysis of the percentage of folding in patch antennas was performed. The folding of patch antennas is expected to become mandatory because patch antennas are integrated and molded according to specified object shapes. The designed antenna was operated at 0.1-5.0 GHz to investigate the folding performance in the frequency range of 1.00-3.78 GHz used in many wireless applications, such as GPS, GSM, and LTE standards. A promising operating frequency for flat (unfold) antennas is 1.42 GHz with an achieved multiband bandwidth of 31.6 MHz, which shifted according to the folding angle 'a' but with good performance. The results of this study can be used to predict the performance of an antenna when it is placed on a product of any shape, according to the designed object pattern.

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“…In our previous research we have investigated challenges and proposed solutions for providing connected healthcare solutions which solve the interoperability and integration challenges of data among various devices and systems. For example, in the CareStore project [12], we proposed an innovative platform for seamless deployment of devices and apps used for personal health monitoring [13]. This research adds intelligence to personal health monitoring.…”

Section: Introductionmentioning

confidence: 99%

AiDHealth: An AI-enabled Digital Health Framework for Connected Health and Personal Health Monitoring

Memon

Abbasi

Rahu

et al. 2023

Preprint

Self Cite

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We live in a digitally connected world inspired by state-of-the-art ICT technologies and networks, inasmuch as the use of digital gadgets and apps is exponentially increasing in all domains of life. In parallel, artificial intelligence has evolved as an essential tool in all sorts of applications and systems such as healthcare systems. Healthcare is the key domain where the use of ICT infrastructure, technologies and artificial intelligence are playing a major role in providing connected and personalized digital health experiences. The vision is to provide intelligent and customized digital health solutions and involve the masses in personal health monitoring. This research proposes AiDHealth as an intelligent personal health monitoring framework based on artificial intelligence for healthcare data analytics and connectivity for personal health monitoring. AiDHealth relies on various machine learning and deep learning models for achieving prediction accuracy in healthcare data analytics. The extensive Pima Indian Diabetes (PID) dataset has been used for investigation. The findings of our experiments illustrate the effectiveness and suitability of the suggested MLPD model. AdaBoost classifier performance has the highest accuracy in prediction when calculated to the individual classifiers. The AdaBoost classifier produced the best accuracy i.e., 0.975%. The results reveal improvements to state-of-the-art procedures in the proposed model. Next, we trained the models and produced a 10-fold cross-validation illness risk index for each sample. Our findings suggest a need for greater experiments to compare the above-mentioned machine learning methods. We identified the AdaBoost classifier and Decision Tree classifiers with the best prediction with an average of 0.975% and a work Curve Area (AUC) of 0.994%. Thus, because the design of the AdaBoost classifier is superior, it can forecast the danger of type 2 diabetes more accurately than the existing algorithms and lead to intelligent prevention and control of diabetes.

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Eye on Patient Care: Continuous Health Monitoring: Design and Implementation of a Wireless Platform for Healthcare Applications

Cited by 17 publications

References 28 publications

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Generating and Detecting Solvable Chaos at Radio Frequencies with Consideration to Multi-User Ranging

Systematic Flexible Antenna Performance Study of V-folding Percentage Influence

AiDHealth: An AI-enabled Digital Health Framework for Connected Health and Personal Health Monitoring

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