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

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 6133

Special Issue Editors

Dr. Mevludin Memedi

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

18 pages, 3688 KiB  
Article
Adapting Young Adults’ In-Shoe Motion Sensor Gait Models for Knee Evaluation in Older Adults: A Study on Osteoarthritis and Healthy Knees
by Chenhui Huang, Kenichiro Fukushi, Haruki Yaguchi, Keita Honda, Yusuke Sekiguchi, Zhenwei Wang, Yoshitaka Nozaki, Kentaro Nakahara, Satoru Ebihara and Shin-Ichi Izumi
Sensors 2025, 25(7), 2167; https://doi.org/10.3390/s25072167 - 28 Mar 2025
Viewed by 305
Abstract
The human knee joint is crucial for mobility, especially in older adults who are susceptible to conditions like osteoarthritis (OA). Traditionally, assessing knee health requires complex gait analysis in clinical settings, which limits opportunities for convenient and continuous monitoring. This study leverages advancements [...] Read more.
The human knee joint is crucial for mobility, especially in older adults who are susceptible to conditions like osteoarthritis (OA). Traditionally, assessing knee health requires complex gait analysis in clinical settings, which limits opportunities for convenient and continuous monitoring. This study leverages advancements in wearable technology to explore the adaptation of models based on in-shoe motion sensors (IMS), initially trained on young adults, for evaluating knee function in older populations, both healthy and with OA. Data were collected from 44 older OA patients, presenting various levels of severity, and 20 healthy older adults, with a focus on key knee indicators: knee angle measures (S1 to S3), temporal gait parameters (S4 and S5), and knee angular jerk cost metrics (S6 to S8). The models effectively identified trends and differences across these indicators between the healthy group and the OA group. Notably, in indicators S1, S2, S3, S7, and S8, the models exhibited a large effect size in correlation with true values. These findings suggest that gait models derived from younger, healthy individuals are possible to be robustly adapted for non-invasive, everyday monitoring of knee health in older adults, offering valuable insights for the early detection and management of knee impairments. However, limitations such as fixed biases due to differences in measurement systems and sensor placement inaccuracies were identified. Future research will aim to enhance model precision by addressing these limitations through domain adaptation techniques and improved sensor calibration. 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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19 pages, 8857 KiB  
Article
Enhanced Vital Parameter Estimation Using Short-Range Radars with Advanced Motion Compensation and Super-Resolution Techniques
by Sewon Yoon, Seungjae Baek, Inoh Choi, Soobum Kim, Bontae Koo, Youngseok Baek, Jooho Jung, Sanghong Park and Min Kim
Sensors 2024, 24(20), 6765; https://doi.org/10.3390/s24206765 - 21 Oct 2024
Viewed by 1209
Abstract
Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the [...] Read more.
Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement. 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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20 pages, 5560 KiB  
Article
Development of Assistance Level Adjustment Function for Variable Load on a Forearm-Supported Robotic Walker
by Yuto Mori, Soichiro Yokoyama, Tomohisa Yamashita, Hidenori Kawamura and Masato Mori
Sensors 2024, 24(19), 6456; https://doi.org/10.3390/s24196456 - 6 Oct 2024
Viewed by 1500
Abstract
With the progression of an aging society, the importance of walking assistance technology has been increasing. The research and development of robotic walkers for individuals requiring walking support are advancing. However, there was a problem that the conventional constant support amount did not [...] Read more.
With the progression of an aging society, the importance of walking assistance technology has been increasing. The research and development of robotic walkers for individuals requiring walking support are advancing. However, there was a problem that the conventional constant support amount did not satisfy the propulsion force required for the walking speed that users wanted. In this study, in order to solve this problem, we propose an algorithm for determining the support amount to maintain the walking speed when the average walking speed of each user is set as the target speed. A robotic walker was developed by attaching BLDC motors to an actual walker, along with a control algorithm for assistance based on sampling-type PID control. The effectiveness of the assistance determination algorithm and the usefulness of the parameters were demonstrated through experiments using weights loaded on the forearm support and target speeds. Subsequently, subject experiments were conducted to verify the ability to maintain target speeds, and a questionnaire survey confirmed that the assistance did not interfere with actual walking. The proposed algorithm for determining the assistance levels demonstrated the ability to maintain target speeds and allowed for adjustments in the necessary level of assistance. 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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16 pages, 3865 KiB  
Article
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
Cited by 1 | Viewed by 1971
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 [...] Read more.
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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