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Advances in IoT, AI and Sensor-Based Technologies for Disease Treatment, Health Promotion and Ageing Well

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Special Issue Editors

Dr. Mevludin Memedi

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Guest Editor

1. Informatics, Business School, Örebro University, 701 82 Örebro, Sweden
2. College of Business, Alfaisal University, Riyadh 11533, Saudi Arabia
Interests: machine learning; quantitative methods; sensors; Internet of Things; patient empowerment

Dr. Vanessa Leung

E-Mail Website
Guest Editor

Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, 3100 St. Pölten, Austria
Interests: digital biomarkers; wearable sensors; extended reality for healthcare; eye-tracking; quantitative methods for digital health

Dr. Vida Groznik

E-Mail Website
Guest Editor

Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, 6000 Koper, Slovenia
Interests: AI in medicine; medical informatics; machine learning; decision support systems; digital biomarkers; eye-tracking

Special Issue Information

Dear Colleagues,

Advancements in the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies have vastly improved remote health monitoring and personalized interventions for health and wellbeing. In disease treatment, sensor technologies can track and analyze individuals' vital health signs, potentially leading to improved and earlier diagnosis, disease prevention, risk stratification, treatment efficacy, and follow-up care. Based on individual digital biomarker profiles, AI can empower patients by providing personalized and tailored pharmacological and non-pharmacological interventions and recommendations and by facilitating improved dialogue between doctors and patients. Integrating sensors and AI into innovations for improving healthcare beyond clinical environments has also allowed interventions for healthy living through active assisted living and digital health technologies for wellbeing.

These technological advancements can potentially revolutionize disease treatment, health promotion, and active ageing. This Special Issue aims to compile original research and review articles on recent advances, technologies, solutions, applications, and challenges in IoT, AI, and sensors applied in healthcare and digital wellbeing.

Potential topics include but are not limited to:

Disease diagnosis and prevention;
Remote health monitoring;
Smart homes and ambient-assisted living for elderly care;
Machine learning, signal processing, and statistical methods for healthcare data analysis;
Ethical, legal, and socioeconomic implications;
Security, privacy, and ethical considerations;
Patient-centered health and patient empowerment;
Challenges and opportunities when integrating new technologies;
Use of chatbots for empowerment, engagement, and personalized care;
Natural language processing for improved patient–doctor communication.

Dr. Mevludin Memedi
Dr. Vanessa Leung
Dr. Vida Groznik
Guest Editors

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Keywords

Internet of Things
artificial intelligence
sensor technologies
digital biomarkers
active assisted living
ageing well
patient monitoring
disease treatment
smart homes
patient empowerment
personalized care
chatbots
natural language processing

Published Papers (1 paper)

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Research

16 pages, 3865 KiB

Open AccessArticle

Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram

by Gašper Slapničar, Jie Su and Wenjin Wang

Sensors 2024, 24(7), 2100; https://doi.org/10.3390/s24072100 - 25 Mar 2024

Viewed by 448

Abstract

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned—the ECG R peak and PPG systolic peak are aligned—before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG. Full article

(This article belongs to the Special Issue Advances in IoT, AI and Sensor-Based Technologies for Disease Treatment, Health Promotion and Ageing Well)

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Advances in IoT, AI and Sensor-Based Technologies for Disease Treatment, Health Promotion and Ageing Well

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