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Sensors
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Recent Innovations in Wearable Sensors for Biomedical Approaches
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Recent Innovations in Wearable Sensors for Biomedical Approaches

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2054

Special Issue Editors

Dr. Martino Giaquinto

E-Mail Website
Guest Editor
Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
Interests: wearable biosensors; biomedical signals; artificial intelligence; edge computing; optical biosensors
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Greco

E-Mail Website
Guest Editor
Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
Interests: information retrieval; internet of things; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Michela Russo

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, University of Naples Federico II, 80125 Napoli, Italy
Interests: wearable biosensors; biomedical signals; motion capture analysis

Special Issue Information

Dear Colleagues,

Wearable biosensors are transforming healthcare by enabling real-time, continuous, non-invasive monitoring of physiological signals. Integrated into devices like smart bands, e-glasses, smart textiles and clinical-grade tools, these technologies are opening up new possibilities for improved health management and personalized medicine.

Promising examples include continuous glucose monitors, wearable ECG/PPG devices for cardiovascular diagnostics and biochemical sweat sensors detecting multiple biomarkers related to physiological conditions. EEG sensors for monitoring brain activity, as well as EMG sensors for neuromuscular activity and inertial measurement units (IMUs) for motion tracking in movement disorders, are further advancing real-time monitoring by analyzing bioelectrical signals and biomechanical data. The convergence of wearable sensing technologies with Artificial Intelligence and the Internet of Medical Things (IoMT) is offering new unprecedented opportunities supporting early diagnosis, disease tracking and personalized rehabilitation, enhancing patient outcomes and clinical decisions.

The key challenges include the integration of wearable technologies into healthcare workflows by developing advanced body-integrated systems, ensuring accuracy and reliability in clinical settings. Managing and interpretating high-dimensional and large volumes data, along with energy-efficient edge computing solutions for real-time anomaly detection and pattern recognition while addressing patient adherence and data security, remain critical.

This Special Issue invites contributions from researchers in the fields of biosensors, artificial intelligence, edge computing and health data security to explore innovations in wearable technologies for personalized healthcare, disease management and rehabilitation.

Dr. Martino Giaquinto
Dr. Luca Greco
Dr. Michela Russo
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. 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

  • novel smart health sensors
  • algorithms for physiological signal processing
  • low-power edge computing for digital health applications
  • secure communication protocols for health data

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Published Papers (3 papers)

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Research

20 pages, 3675 KB  
Article
Design and Evaluation of a Pneumatic-Actuated Active Balance Board for Sitting Postural Control
by Erkan Kaplanoglu, Max Jordon, Jeremy Bruce and Gazi Akgun
Sensors 2025, 25(23), 7101; https://doi.org/10.3390/s25237101 - 21 Nov 2025
Viewed by 346
Abstract
Chronic low back pain (cLBP) is a pervasive and debilitating condition that can result in motor control deficits and often leads to opioid dependence. Conventional rehabilitation approaches generally rely on internally driven tasks, which fail to capture adaptive motor responses to external perturbations. [...] Read more.
Chronic low back pain (cLBP) is a pervasive and debilitating condition that can result in motor control deficits and often leads to opioid dependence. Conventional rehabilitation approaches generally rely on internally driven tasks, which fail to capture adaptive motor responses to external perturbations. This study focuses on the design and evaluation of a pneumatic-actuated active balance board integrating pneumatic artificial muscles (PAMs), electromyography (EMG), and inertial measurement units (IMUs) to assess seated postural control responses. With PAM-powered perturbations, the balance board introduces controlled challenges to evaluate postural control dynamics and motor adaptation. EMG sensors monitor muscle activity in key postural muscles, while IMU systems track movement responses. The system was evaluated through an experimental trial with 15 healthy participants performing balance tasks on both a passive and active balance board. The active balance board’s effectiveness is assessed using signal processing techniques, including root mean square (RMS) analysis, Fast Fourier Transform (FFT), autoregressive (AR) modeling, and the Welch t-test. Experimental trials were conducted with healthy participants to establish baseline performance. Results demonstrate that the active balance board successfully induces adaptive motor responses, with higher EMG activation levels compared to passive boards. Frequency-domain analyses confirm significant differences in muscle activation patterns, supporting the hypothesis that external perturbations enhance postural control retraining. The pneumatic-actuated balance board presented in this study represents a novel approach to postural control assessment that may be applied in future rehabilitation studies involving individuals with cLBP, addressing the limitations of traditional methods. Future research will focus on clinical trials with cLBP patients to further evaluate its therapeutic efficacy and long-term benefits in rehabilitation. Full article
(This article belongs to the Special Issue Recent Innovations in Wearable Sensors for Biomedical Approaches)
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24 pages, 3574 KB  
Article
Monitoring the Impact of Two Pedagogical Models on Physical Load in an Alternative School Sport Using Inertial Devices
by Olga Calle, Antonio Antúnez, Sergio González-Espinosa, Sergio José Ibáñez and Sebastián Feu
Sensors 2025, 25(18), 5929; https://doi.org/10.3390/s25185929 - 22 Sep 2025
Viewed by 667
Abstract
(1) Background: Physical Education sessions subject students to various physical and physiological demands that teachers must understand to design interventions aimed at improving health and fitness. This study aimed to quantify and compare external and internal load before and after implementing two intervention [...] Read more.
(1) Background: Physical Education sessions subject students to various physical and physiological demands that teachers must understand to design interventions aimed at improving health and fitness. This study aimed to quantify and compare external and internal load before and after implementing two intervention programs: one based on the Game-Centered Model and another Hybrid Model that combines the Game-Centered Model with the Sport Education Model. (2) Methods: A total of 47 first-year secondary school students participated, divided into two naturally formed groups. Pre- and post-intervention assessments involved 4 vs. 4 matches monitored using WIMU Pro™ inertial measurement units and heart rate monitors to collect kinematic, neuromuscular, and physiological data. The combined use of inertial sensors and heart rate monitors enabled the objective quantification of students’ physical demands. (3) Results: No significant improvements were observed between pre- and post-tests, possibly due to the short duration of the interventions. However, the Hybrid Model generated higher levels of external load, both kinematic and neuromuscular, in the post-test. (4) Conclusions: The Hybrid Model appears more effective in increasing students’ physical engagement. Inertial sensors represent a valid and practical tool for monitoring and adjusting instructional strategies in school-based Physical Education. Full article
(This article belongs to the Special Issue Recent Innovations in Wearable Sensors for Biomedical Approaches)
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18 pages, 5418 KB  
Article
Validity of a Novel Algorithm to Compute Spatiotemporal Parameters Based on a Single IMU Placed on the Lumbar Region
by Giuseppe Prisco, Giuseppe Cesarelli, Maria Romano, Marina Picillo, Carlo Ricciardi, Fabrizio Esposito, Paolo Barone, Mario Cesarelli and Leandro Donisi
Sensors 2025, 25(18), 5822; https://doi.org/10.3390/s25185822 - 18 Sep 2025
Viewed by 596
Abstract
Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and [...] Read more.
Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors. Full article
(This article belongs to the Special Issue Recent Innovations in Wearable Sensors for Biomedical Approaches)
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