Internet of Things for E-health

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 1582

Special Issue Editor

Department of Public and Community Health, University of West Attica, 11521 Athens, Greece
Interests: rehabilitation; health informatics; e-health; telemedicine; assistive technologies; users satisfaction assessment; strategic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Integration of the Internet of Things (IoT) in the field of e-health has brought about transformative advancements in healthcare; the convergence of the IoT and e-health has opened up new possibilities for remote patient monitoring, personalized healthcare, and efficient medical interventions. By connecting medical devices, wearables, and sensors to the internet, the IoT enables seamless communication and data exchange between patients, healthcare providers, and medical systems. This connectivity allows for remote patient monitoring, real-time health data collection, and analysis, leading to more efficient and personalized healthcare services. This continuous stream of information allows for proactive healthcare management, early detection of health issues, and timely interventions. IoT-enabled devices can transmit data securely to healthcare professionals, enabling remote monitoring and reducing the need for frequent hospital visits. The IoT in e-health facilitates proactive healthcare management by continuously monitoring patients' vital signs, medication adherence, and other relevant parameters. With remote monitoring capabilities, healthcare professionals can promptly detect any abnormalities or changes in a patient's health status and intervene accordingly, even from a distance. Furthermore, the IoT in e-health can enhance patient engagement and empowerment by providing them with real-time feedback, personalized recommendations, and the ability to monitor their progress. The ability to analyze vast amounts of data generated by IoT devices also facilitates predictive analytics and personalized treatment plans. As the Internet of Things continues to advance, its integration with e-health promises to enhance healthcare accessibility, improve patient outcomes, and transform the way healthcare services are delivered and experienced.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Remote Patient Monitoring: Exploring the role of the IoT in enabling the remote monitoring of patients' health conditions, vital signs, and medication adherence.
  • Wearable Devices and IoT in mHealth: Investigating the role of wearable devices, such as smartwatches, fitness trackers, and biosensors, in conjunction with the IoT for monitoring health metrics, activity tracking, and delivering personalized health interventions.
  • IoT-enabled Telemedicine: Investigating how IoT technologies enhance telemedicine by facilitating virtual consultations, remote diagnostics, and the telemonitoring of patients.
  • Data Security and Privacy in IoT-driven E-Health: Analyzing the challenges and best practices for ensuring data security and protecting patient privacy in IoT-based e-health systems.
  • Smart Hospitals: Exploring the implementation of IoT technologies in hospital settings to improve operational efficiency, asset tracking, patient flow management, and patient safety.
  • IoT-enabled Medication Management: Investigating the use of IoT devices and systems to enhance medication management, including smart pill dispensers, medication adherence monitoring, and remote medication tracking.
  • IoT and Chronic Disease Management: Examining how IoT technologies can assist in managing chronic diseases, such as diabetes, asthma, and hypertension, through remote monitoring, personalized treatment plans, and behavior tracking.
  • IoT in Emergency Medical Services: Exploring the role of the IoT in emergency medical services, including the real-time tracking of ambulances, remote patient monitoring during transportation, and IoT-enabled emergency response systems.
  • Healthcare Data Analytics with IoT: Discussing the application of IoT-generated healthcare data in data analytics and predictive modeling to improve healthcare decision making and outcomes.
  • Ethical and Legal Considerations in IoT-driven E-Health: Examining the ethical and legal implications of using IoT technologies in e-health, including patient consent, data ownership, and the potential risks associated with IoT devices and systems.

Dr. Yiannis Koumpouros
Guest Editor

Manuscript Submission Information

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Keywords

  • IoT
  • e-health
  • mhealth
  • telehealth
  • sensors
  • healthcare

Published Papers (1 paper)

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Research

12 pages, 4297 KiB  
Article
Wearable IoT System for Hand Function Assessment Based on EMG Signals
by Zhenhao Zhi and Qun Wu
Electronics 2024, 13(4), 778; https://doi.org/10.3390/electronics13040778 - 16 Feb 2024
Viewed by 443
Abstract
Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of [...] Read more.
Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of hand function, with a focus on a wearable wristband. The system, enhanced by cloud technology, offers comprehensive solutions for remote health management and therapeutic services. Firstly, it uses electromyography (EMG) signals from the arm to assess hand function. By employing sophisticated classification and regression models, this system can automatically identify user gestures and accurately measure grip strength. Additionally, the integration of additional sensor data ensures that the system fulfills essential criteria for hand function assessment. Leaving conventional grip strength classification methods, this study explored four distinct regression models to accurately represent the grip strength curve. The findings reveal that the Random Forest Regression (RFR) model is the most effective, achieving an R2 score of 0.9563 on the test data. This significant outcome not only confirms the practicality of the wearable wristband, which relies on EMG signals, but also underscores the potential of the IoT system in assessing hand function. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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