Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
3.7
Journals
Bioengineering
Special Issues
Advancements in Artificial Intelligence for Wearable Devices: A New Perspective...
share announcement

Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications—2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1982

Special Issue Editors

Prof. Dr. Maria Giovanna Bianco

E-Mail Website
Guest Editor
Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: biomedical signal processing (EMG, EEG, ECoG, and LFP); wearable medical devices; machine learning; structural MRI analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Syed Kamrul Islam

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, The University of Missouri-Columbia (MIZZOU), Columbia, MO 65211, USA
Interests: analog/mixed-signal integrated circuits; monolithic sensors; semiconductor devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Salvatore Andrea Pullano

E-Mail Website
Guest Editor
Biomedical Applications Technologies & Sensors (BATS) Laboratory, Department of Health Sciences, Magna Graecia University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: biomedical signal processing; ion-sensitive field-effect transistors; PH sensors; pyroelectric and piezoelectric sensors; ionoelectronic interfaces; nanoporous materials for biomedical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, wearable devices (WDs) have emerged as an interesting frontier for innovation, poised to shape the future of human interaction with technology. The progress in sensing technologies and integrated electronic circuits has facilitated the creation of sophisticated devices capable of integrating sensors.

Sensor technology and electronic interfaces have been receiving growing interest from scientists as promising developments have been made. The high degree of miniaturization of classic measurement techniques has led to the realization of complex analytical systems including integrated sensors as in lab-on-a-chip. This new class of sensors extensively contributes to a broad range of activities with widespread applications including biology and medicine. Recent advances in artificial intelligence (AI) provide opportunities to reveal hidden information in biosignals that are not apparent using conventional methods of analysis. Beyond mere accessories, wearables play a pivotal role in healthcare, and the incorporation of AI technologies has played a crucial role in rapidly advancing the field.

This Special Issue aims to collect original scientific papers that apply novel or state-of-the-art AI technologies on wearable devices. We encourage original scientific papers about the application of machine learning and deep learning and real-time monitoring using WDs. Systematic reviews or meta-analyses are also welcome.

Prof. Dr. Maria Giovanna Bianco
Prof. Dr. Syed Kamrul Islam
Dr. Salvatore Pullano
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 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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • wearable devices
  • artificial ilntelligence
  • machine learning
  • biomedical signal processing
  • deep learning
  • bio-inspired systems
  • biosensor devices

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1317 KB  
Article
Digital Gait Biomarkers for Parkinson’s Disease: Subject-Wise Validated Explainable AI Framework Using Vertical Ground Reaction Force Signals
by Moonhyeok Choi, Jaehyun Jo and Jinhyoung Jeong
Bioengineering 2026, 13(3), 360; https://doi.org/10.3390/bioengineering13030360 - 19 Mar 2026
Viewed by 404
Abstract
Parkinson’s disease (PD) is associated with progressive gait deterioration; however, widely used clinical scales such as the Hoehn & Yahr (H&Y) stage are limited in capturing continuous severity changes due to subjectivity and discrete grading. This study proposes a two-stage explainable AI framework [...] Read more.
Parkinson’s disease (PD) is associated with progressive gait deterioration; however, widely used clinical scales such as the Hoehn & Yahr (H&Y) stage are limited in capturing continuous severity changes due to subjectivity and discrete grading. This study proposes a two-stage explainable AI framework using vertical ground reaction force (VGRF) signals to achieve reproducible PD detection and continuous severity estimation. In the first stage, three deep learning models, temporal convolutional network (TCN), BiGRU with attention, and FCNN-Transformer, were trained using windowed VGRF signals under repeated subject-wise data segmentation. All models achieved high discrimination performance (AUC ≥ 0.93), with FCNN-Transformer showing the highest mean AUC (0.940) and statistically superior performance (paired Wilcoxon test, p < 0.05). Stability-based explainable AI using Integrated Gradients consistently identified variability-related VGRF features as the most informative, which were also significantly different between groups at the data level (p < 0.001, FDR-corrected). In the second stage, XGBoost regression was applied to PD subjects to predict continuous H&Y severity, achieving strong correlation with clinical grades (Spearman ρ = 0.921, p < 0.001), low error (MAE = 0.158, RMSE = 0.241), and high determination (R2 = 0.953). This shows that gait-based features are a sensitive enough signal to continuously quantify disease progression. In addition, in the TREND prospective longitudinal cohort (n = 696), wearable walking indicators differed significantly from those of non-patients prior to diagnosis, and a decline in walking pace was observed approximately four years before Parkinson’s disease diagnosis, providing the basis for early screening and monitoring using gait-based digital biomarkers. These results demonstrate that gait-based digital biomarkers can objectively quantify both PD presence and disease progression. The proposed framework provides a reproducible, explainable, and clinically interpretable AI-based decision support approach for PD assessment. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications—2nd Edition)
►▼ Show Figures

Figure 1

17 pages, 1504 KB  
Article
Similarity Gait Networks with XAI for Parkinson’s Disease Classification: A Pilot Study
by Maria Giovanna Bianco, Camilla Calomino, Marianna Crasà, Alessia Cristofaro, Giulia Sgrò, Fabiana Novellino, Salvatore Andrea Pullano, Syed Kamrul Islam, Jolanda Buonocore, Aldo Quattrone, Andrea Quattrone and Rita Nisticò
Bioengineering 2026, 13(2), 151; https://doi.org/10.3390/bioengineering13020151 - 28 Jan 2026
Viewed by 513
Abstract
Parkinson’s disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals [...] Read more.
Parkinson’s disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals were acquired using Xsens inertial sensors from 51 patients with PD and 53 healthy controls. For each participant, subject-specific kinematic networks were constructed by modeling inter-segment similarities through Jensen–Shannon divergence, from which global and local graph-theoretical metrics were extracted. A machine learning pipeline incorporating voting feature selection, and XGBoost classification was evaluated using a nested cross-validation design. The model achieved robust performance (AUC = 0.87), and explainability analyses using SHAP identified a subset of 13 features capturing alterations in velocity, inter-segment connectivity, and network centrality. PD was characterized by increased positional variability, reduced distal limb velocity, and a redistribution of network centrality towards proximal body segments. These features were associated with clinical severity, confirming their physiological relevance. By integrating graph-theoretical modeling, explainable artificial intelligence, and machine learning methodology, this work provides a method of discovering quantitative biomarkers capturing alterations in motor coordination. These findings highlight the potential of ML and kinematic networks to support objective motor assessment in PD. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications—2nd Edition)
►▼ Show Figures

Graphical abstract

19 pages, 1753 KB  
Article
Multimodal Physiological Monitoring Using Novel Wearable Sensors: A Pilot Study on Nocturnal Glucose Dynamics and Meal-Related Cardiovascular Responses
by Emi Yuda, Yutaka Yoshida, Hiroyuki Edamatsu and Junichiro Hayano
Bioengineering 2026, 13(1), 69; https://doi.org/10.3390/bioengineering13010069 - 8 Jan 2026
Viewed by 767
Abstract
This pilot study investigated multimodal physiological monitoring using minimally invasive and wearable sensors across two experimental settings. Experiment 1 involved five healthy adults (1 female) who simultaneously wore an interstitial fluid glucose (ISFG) sensor and a ring-type wearable device during sleep (00:00–06:00). Time-series [...] Read more.
This pilot study investigated multimodal physiological monitoring using minimally invasive and wearable sensors across two experimental settings. Experiment 1 involved five healthy adults (1 female) who simultaneously wore an interstitial fluid glucose (ISFG) sensor and a ring-type wearable device during sleep (00:00–06:00). Time-series analyses revealed that ISFG levels decreased during sleep in four of the five participants. ISFG values were significantly lower in the latter half of the sleep period compared with the first half (0–3 h vs. 3–6 h, p = 0.01, d = 2.056). Four participants also exhibited a mild reduction in SpO2 between 03:00–04:00. These results suggest that nocturnal ISFG decline may be associated with subtle oxygen-saturation dynamics. Experiment 2 examined whether wearable sensors can detect physiological changes across meal-related phases. Nine male participants were monitored for heart rate (HR) and skin temperature during three periods: pre-meal (Phase 1: 09:00–09:30), during meal consumption (Phase 2: 12:30–13:00), and post-meal (Phase 3: 13:00–13:30). A paired comparison demonstrated a significant difference in median HR between Phase 1 and Phase 2 (p = 0.029, d = 0.812), indicating a large effect size. In contrast, HR–temperature correlation was weak and not statistically significant (Pearson r = 0.067, p = 0.298). Together, these findings demonstrate that multimodal wearable sensing can capture both nocturnal glucose fluctuations and meal-induced cardiovascular changes. This integrative approach may support real-time physiological risk assessment and future development of remote healthcare applications. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Wearable Devices: A New Perspective on Healthcare Applications—2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop