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Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots...
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Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots in Health Monitoring and Care

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 4667

Special Issue Editors

Prof. Dr. Pedro J. S. Cardoso

E-Mail Website
Guest Editor
NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Instituto Superior de Engenharia (ISE), Universidade do Algarve, 8005-139 Faro, Portugal
Interests: operational research; machine learning; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João M. F. Rodrigues

E-Mail Website1 Website2
Guest Editor
NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Instituto Superior de Engenharia (ISE), Universidade do Algarve, 8005-139 Faro, Portugal
Interests: computer vision; human–computer interaction; human–machine cooperation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Javier Medina Quero

E-Mail Website
Guest Editor
Department of Computer Engineering, Automation and Robotics, University of Granada, 18071 Granada, Spain
Interests: machine learning; human activity recognition; Internet of Things; fuzzy logic; sensor fusion
Dr. Aurora Polo Rodríguez

E-Mail Website
Guest Editor Assistant
Department of Computer Engineering, Automation and Robotics, University of Granada, 18071 Granada, Spain
Interests: smart environments; ambient intelligence; Internet of Things (IoT); E-Health; activity recognition; machine learning; human-centered computing; assistive technologies

Special Issue Information

Dear Colleagues,

Rapid advancements in artificial intelligence (AI), machine learning (ML), data science, the Internet of Things (IoT), and assistive robotics are revolutionizing the healthcare sector. These technologies are pivotal in addressing the pressing healthcare challenges posed by an aging population, the prevalence of chronic conditions, and the need for personalized, efficient, and accessible care. This Special Issue aims at exploring the transformative potential of these technologies in health monitoring and care.

We invite original research articles, reviews, and case studies that focus on, but are not limited to, the following topics:

1. AI and ML in Health Monitoring:
  • Development and application of AI models for real-time health monitoring and the early detection of health deterioration.
  • Machine learning algorithms for predictive analytics in healthcare.
  • AI-driven personalized care plans and interventions.
2. IoT in Healthcare:
  • Integration of IoT devices for continuous health monitoring.
  • IoT-based systems for remote patient monitoring and management.
  • Security and privacy issues in IoT healthcare applications.

3. Data Science in Health:

  • Big data analytics for healthcare insights and decision-making.
  • Data integration and interoperability in health information systems.
  • Data-driven approaches to improve patient outcomes and healthcare efficiency.
4. Wearable and Assistive Technologies:
  • Development and evaluation of wearable health devices.
  • Assistive robots for enhancing patient care and autonomy.
  • User-centered design and usability studies of health technologies.

5. Assistive Robots in Healthcare:

  • Design and implementation of assistive robots for patient support and care.
  • Human–robot interaction and its impact on patient well-being.
  • Case studies on the deployment of assistive robots in clinical and home settings.

6. Interdisciplinary Approaches:

  • Collaborative frameworks for integrating technology in healthcare.
  • Ethical, legal, and social implications of AI, IoT, and robotics in health.
  • Case studies and pilot projects demonstrating successful implementations.

Authors are invited to submit manuscripts that make significant contributions to the field. All submissions will undergo a rigorous peer-review process to ensure the highest quality of research. Manuscripts should be prepared according to the journal's guidelines and submitted through the journal's online submission system.

We look forward to your contributions to this Special Issue, which aims at showcasing the latest innovations and research in the application of AI, ML, IoT, and assistive robots in health monitoring and care.

Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. João M. F. Rodrigues
Prof. Dr. Javier Medina Quero
Guest Editors

Dr. Aurora Polo Rodríguez
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 2400 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

  • artificial intelligence
  • machine learning
  • Internet of Things
  • data sciences
  • wearable and assistive technologies
  • assistive robots
  • health monitoring and healthcare

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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

19 pages, 599 KB  
Article
Reducing Hallucinations in Medical AI Through Citation Enforced Prompting in RAG Systems
by Lukasz Pawlik and Stanislaw Deniziak
Appl. Sci. 2026, 16(6), 3013; https://doi.org/10.3390/app16063013 - 20 Mar 2026
Viewed by 1216
Abstract
The safe integration of Large Language Models in clinical environments requires strict adherence to verified medical evidence. As part of the PARROT AI project, this study provides a systematic evaluation of how prompting strategies affect the reliability of Retrieval-Augmented Generation (RAG) pipelines using [...] Read more.
The safe integration of Large Language Models in clinical environments requires strict adherence to verified medical evidence. As part of the PARROT AI project, this study provides a systematic evaluation of how prompting strategies affect the reliability of Retrieval-Augmented Generation (RAG) pipelines using the MedQA USMLE benchmark (N=500). Four prompting strategies were examined: Baseline (zero-shot), Neutral, Expert Chain-of-Thought (Expert-CoT) with structured clinical reasoning, and StrictCitations with mandatory evidence grounding. The experiments covered six modern model architectures: Command R (35B), Gemma 2 (9B and 27B), Llama 3.1 (8B), Mistral Nemo (12B), and Qwen 2.5 (14B). Evaluation was conducted using the Deterministic RAG Evaluator, providing an objective assessment of grounding through the Unsupported Sentence Ratio (USR) based on TF-IDF and cosine similarity. The results indicate that structured reasoning in the Expert-CoT strategy significantly increases USR values (reaching 95–100%), as models prioritize internal diagnostic logic over verbatim context. In contrast, the StrictCitations strategy, while maintaining high USR due to the conservative evaluation threshold, achieves the highest level of verifiable grounding and source adherence. The analysis identifies a statistically significant Verbosity Signal (r=0.81,p<0.001), where increased response length serves as a proxy for model uncertainty and parametric leakage, a pattern particularly prominent in Llama 3.1 and Gemma 2. Overall, the findings demonstrate that prompting strategy selection is as critical for clinical reliability as model architecture. This work delivers a reproducible framework for the development of trustworthy medical AI assistants and highlights citation-enforced prompting as a vital mechanism for improving clinical safety. Full article
(This article belongs to the Special Issue Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots in Health Monitoring and Care)
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20 pages, 2458 KB  
Article
Efficient and Personalized Federated Learning for Human Activity Recognition on Resource-Constrained Devices
by Abdul Haseeb, Ian Cleland, Chris Nugent and James McLaughlin
Appl. Sci. 2026, 16(2), 700; https://doi.org/10.3390/app16020700 - 9 Jan 2026
Viewed by 671
Abstract
Human Activity Recognition (HAR) using wearable sensors enables impactful applications in healthcare, fitness, and smart environments, but it also faces challenges related to data privacy, non-independent and identically distributed (non-IID) data, and limited computational resources on edge devices. This study proposes an efficient [...] Read more.
Human Activity Recognition (HAR) using wearable sensors enables impactful applications in healthcare, fitness, and smart environments, but it also faces challenges related to data privacy, non-independent and identically distributed (non-IID) data, and limited computational resources on edge devices. This study proposes an efficient and personalized federated learning (PFL) framework for HAR that integrates federated training with model compression and per-client fine-tuning to address these challenges and support deployment on resource-constrained devices (RCDs). A convolutional neural network (CNN) is trained across multiple clients using FedAvg, followed by magnitude-based pruning and float16 quantization to reduce model size. While personalization and compression have previously been studied independently, their combined application for HAR remains underexplored in federated settings. Experimental results show that the global FedAvg model experiences performance degradation under non-IID conditions, which is further amplified after pruning, whereas per-client personalization substantially improves performance by adapting the model to individual user patterns. To ensure realistic evaluation, experiments are conducted using both random and temporal data splits, with the latter mitigating temporal leakage in time-series data. Personalization consistently improves performance under both settings, while quantization reduces the model footprint by approximately 50%, enabling deployment on wearable and IoT devices. Statistical analysis using paired significance tests confirms the robustness of the observed performance gains. Overall, this work demonstrates that combining lightweight model compression with personalization providing an effective and practical solution for federated HAR, balancing accuracy, efficiency, and deployment feasibility in real-world scenarios. Full article
(This article belongs to the Special Issue Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots in Health Monitoring and Care)
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12 pages, 578 KB  
Article
Can Hospitals Cooperate to Improve Predictions Without Sharing Data? A Federated Learning Approach for Frailty Screening
by Matteo Leghissa, Álvaro Carrera and Carlos Á. Iglesias
Appl. Sci. 2025, 15(18), 9939; https://doi.org/10.3390/app15189939 - 11 Sep 2025
Cited by 1 | Viewed by 870
Abstract
Traditionally, machine learning models in healthcare rely on centralized strategies using raw data. This poses limitations due to the amount of available data, which becomes hard to aggregate due to privacy concerns. Federated learning has been emerging as a new paradigm to improve [...] Read more.
Traditionally, machine learning models in healthcare rely on centralized strategies using raw data. This poses limitations due to the amount of available data, which becomes hard to aggregate due to privacy concerns. Federated learning has been emerging as a new paradigm to improve model performance. It exploits information on the parameters from other clients while never sharing personal data from the patients. We present a proof-of-concept of federated learning techniques in the case of an automated screening tool for frailty in the older population. We used a frailty-specific dataset called FRELSA, with patients from nine regions of the UK used to simulate a scenario with regional hospitals. We compared three different strategies: separate regional training with no communication; federated averaging, the most widely used strategy for healthcare; and finally, global training on the full dataset for comparison. All three strategies were validated with two architectures: logistic regression and a neural network. Results show that federated strategies outperform local training and achieve global-like performance while preserving patient privacy. For Logistic Regression, the global validation F-score was 0.737 and the federated aggregated score was 0.735, offering improvement in seven of the nine regions. For Multi Layer Perceptron, the global validation F-score was 0.843 and the federated aggregated score was 0.834, improving in all nine regional models. The federated strategy is equivalent to pooling all the data together while avoiding all complications related to data privacy and sharing. The results of this study show that the proposed strategy is a viable method for improving frailty screening in healthcare systems. Full article
(This article belongs to the Special Issue Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots in Health Monitoring and Care)
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13 pages, 3685 KB  
Article
A Controlled Variation Approach for Example-Based Explainable AI in Colorectal Polyp Classification
by Miguel Filipe Fontes, Alexandre Henrique Neto, João Dallyson Almeida and António Trigueiros Cunha
Appl. Sci. 2025, 15(15), 8467; https://doi.org/10.3390/app15158467 - 30 Jul 2025
Viewed by 1034
Abstract
Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption [...] Read more.
Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption barriers due to their ‘black box’ nature, limiting interpretability. This study presents an example-based explainable artificial intehlligence (XAI) approach using Pix2Pix to generate synthetic polyp images with controlled size variations and LIME to explain classifier predictions visually. EfficientNet and Vision Transformer (ViT) were trained on datasets of real and synthetic images, achieving strong baseline accuracies of 94% and 96%, respectively. Image quality was assessed using PSNR (18.04), SSIM (0.64), and FID (123.32), while classifier robustness was evaluated across polyp sizes. Results show that Pix2Pix effectively controls image attributes like polyp size despite limitations in visual fidelity. LIME integration revealed classifier vulnerabilities, underscoring the value of complementary XAI techniques. This enhances DL model interpretability and deepens understanding of their behaviour. The findings contribute to developing explainable AI tools for polyp classification and CRC diagnosis. Future work will improve synthetic image quality and refine XAI methodologies for broader clinical use. Full article
(This article belongs to the Special Issue Innovative Applications of AI, Machine Learning, IoT, and Assistive Robots in Health Monitoring and Care)
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