IoT-based eHealth data acquisition system

Pap, Iuliu Alexandru; Oniga, Stefan; Orha, Ioan; Alexan, Alexandru

doi:10.1109/aqtr.2018.8402711

Cited by 16 publications

(5 citation statements)

References 12 publications

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“…The authors intend to extend upon their work by adding small sensors embedded with mobility features to help patients carry the device for greater periods of time. In [29], challenges were encountered while developing an eHealth system that can integrate different sensors seamlessly as discussed. A modular approach was adopted to ensure improved stability and simplified addition of extra sensors.…”

Section: Remote Patient Monitoringmentioning

confidence: 99%

Visualization of Remote Patient Monitoring System Based on Internet of Medical Things

et al. 2023

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Remote patient monitoring (RPM) has become a crucial tool for healthcare professionals in the monitoring and management of patients, particularly for patients with chronic illnesses. RPM has undergone improvements in its capability to deliver real-time data and information to healthcare practitioners as the Internet of Medical Things (IoMT) devices have become more widely available. However, managing and analyzing such a large volume of data still remains a difficult task. The visualization method suggested in this article enables healthcare professionals to examine data gathered by IoMT devices in real-time. Healthcare professionals may monitor patient health status and identify any data irregularities thanks to the system’s dashboard. To assess the system’s usability and user satisfaction, we employed both the Post-Study System Usability Questionnaire (PSSUQ) and the System Usability Scale (SUS). The outcomes of the PSSUQ and SUS assessments revealed that the suggested visualization system scored higher than the control group, demonstrating the system’s usability, accuracy, and dependability as well as its user-friendliness and intuitive interface. The visualization system can boost the effectiveness and efficiency of remote patient monitoring, resulting in better patient care and lower healthcare costs.

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Section: Remote Patient Monitoringmentioning

confidence: 99%

Visualization of Remote Patient Monitoring System Based on Internet of Medical Things

et al. 2023

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“…Liang et al [8] proposed an effective system model integrating physiological sensors, transmission components, and processing capabilities that provide continuous health monitoring. Pap et al in their work [9] proposed an implementation of ehealth system based IoT technology offering local and remote monitoring capabilities. Moreover, in the work [10] the authors introduced a developed wearable wireless monitoring device aims to measures patient temperature and pulse rate and enabling the communication with healthcare provider over cellular network.…”

Section: Related Workmentioning

confidence: 99%

Security aspects related Intelligent e-Health Systems

2023

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Current health situations and crises require physicians and researchers to convert traditional healthcare services to a new generation of digital healthcare known as e-health smart systems that operate close to the end user with the objective to provide medical assistance using advanced technologies including Internet of Medical Things technology, edge computing, and cloud computing paradigms. However, this system architecture reveals limitations in terms of latency, bandwidth, power consumption as well as security and privacy of collected data. Through this paper, a presentation of different healthcare monitoring system layers has been defined, in addition to listed challenges faced by the deployment of this technology, while providing an introduction of security risks that threaten the user data safety alongside the e-health layers. Moreover, a summarized discussion of Blockchain technology came over to provide a secure decentralized architecture in sharing data across allowed entities.

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“…In our previous work with EEG signals [ 39 , 40 ], we used the same e-Health sensor platform as in [ 41 ], where arrhythmia detection is possible through ML algorithms using a Raspberry Pi, an Arduino and the e-Health Sensor Platform v1.0. The e-Health sensor platform allows an Arduino or the Raspberry Pi through an adaptor shield to collect data from blood pressure monitors, pulse oximeters, galvanic skin response, airflow, and temperature sensors.…”

Section: Overview Of Healthcare Applicationsmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

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IoT-based eHealth data acquisition system

Cited by 16 publications

References 12 publications

Visualization of Remote Patient Monitoring System Based on Internet of Medical Things

Visualization of Remote Patient Monitoring System Based on Internet of Medical Things

Security aspects related Intelligent e-Health Systems

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

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