Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Novel Optical Biosensors in Biomechanics and Physiology

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 6163

Share This Special Issue

Special Issue Editors

Prof. Dr. Keat Ghee Ong

E-Mail Website
Guest Editor

Department of Bioengineering, Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR 97403, USA
Interests: implantable sensors; wireless sensors; electronic devices; magneto-elastic materials
Special Issues, Collections and Topics in MDPI journals

Dr. Michael McGeehan

E-Mail Website
Guest Editor Assistant

Department of Bioengineering, Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR 97403, USA
Interests: wireless sensors with orthopedic applications

Special Issue Information

Dear Colleagues,

Optical-based sensing technologies are broadly useful for measuring physiological signals and biomechanical motions and forces. This Special Issue will cover novel sensors and the novel uses of existing sensors in these areas. We are seeking contributions that focus on topics of interest here, which include, but are not limited to, new sensor designs, novel uses for existing sensors, optimizing outputs or signal processing techniques to improve the usefulness of sensors, and evaluating sensors in real-world settings. Wearable, implantable, and integrated optical sensing technologies are of particular interest. Both original research and review papers are welcome.

Prof. Dr. Keat Ghee Ong
Guest Editor

Dr. Michael McGeehan
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. 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

wearable sensors
implantable sensors
biomechanics
physiology
optoelectronics
force
motion
kinematics
kinetics
assistive devices
biomechatronics

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.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

16 pages, 4654 KB

Open AccessArticle

Knee Joint Motion Detection Based on Demodulation of Overlapping Spectrum Using Fiber Bragg Grating Sensor

by Linlin Fan, Lingzhen Yang, Juanfen Wang, Weijie Ding, Huizhi Ren and Chao Zhou

Sensors 2026, 26(11), 3341; https://doi.org/10.3390/s26113341 - 25 May 2026

Abstract

This study proposes a knee joint motion detection method based on overlapping spectrum demodulation using fiber Bragg grating (FBG) technology. A flexible FBG encapsulated with polydimethylsiloxane (PDMS) is attached to the joint surface. Axial strain in the FBG sensor is generated due to the bending and extension movements of the joint, which leads to a central reflection wavelength shift of the FBG sensor. The overlapping spectrum between the FBG reflection and the output of a tunable fiber laser is related to the wavelength shift of the FBG. The variation is expressed as the changes in reflected optical power received by an optical power meter. It transforms complex spectral analysis into intuitive optical power measurement for demodulating the reflected wavelength of the FBG sensor. The relationship between the optical power of the overlapping spectrum and wavelength shift of the FBG induced by joint motion is theoretically and experimentally analyzed. The real-time demodulation of joint motion is realized based on this relationship. Experimental results demonstrate that the system exhibits good repeatability in monitoring knee joint motion. The performance and practical potential of the system are evaluated through a quantitative comparison with existing techniques and an analysis of its current limitations. Full article

(This article belongs to the Special Issue Novel Optical Biosensors in Biomechanics and Physiology)

► Show Figures

Figure 1

29 pages, 4549 KB

Open AccessArticle

Smart Sensor-Driven Gait Rehabilitation Walker Using Machine Learning for Predictive Home-Based Therapy

by Gokul Manavalan, Yuval Arnon, A. N. Nithyaa and Shlomi Arnon

Sensors 2026, 26(8), 2547; https://doi.org/10.3390/s26082547 - 21 Apr 2026

Viewed by 591

Abstract

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

(This article belongs to the Special Issue Novel Optical Biosensors in Biomechanics and Physiology)

► Show Figures

Graphical abstract

18 pages, 1039 KB

Open AccessArticle

In Vivo (In)Stability Shoulder Assessment in Healthy Active Adults Using Force Plates and a Motion Capture System: A Cross-Sectional Study

by Laura Ramírez-Pérez, Eric Yung-Sheng Su, Antonio Ignacio Cuesta-Vargas and Graham K. Kerr

Sensors 2025, 25(17), 5333; https://doi.org/10.3390/s25175333 - 27 Aug 2025

Cited by 1 | Viewed by 1576

Abstract

The assessment of shoulder stability is a great challenge in sports medicine. There is a lack of objective tools to assess functional shoulder stability in sports with high demands on the upper limb. This cross-sectional study recruited twenty healthy adults to analyze the use of a force platform in a push-up analysis as a valid tool for estimating glenohumeral stability. For this purpose, the subjects performed one strength task based on a maximum lateral abduction against a dynamometer. They also performed three variations of a push-up task on force plates with movements recorded by a 3D motion capture system. The results showed that healthy adults present similar movement patterns during push-ups, without differences in terms of stability between sexes, although males showed greater values in lateral abduction strength (left: 63.2 vs. 36.8; p < 0.001; right: 64.2 vs. 38.9; p < 0.001) and ground reaction force peak in the three push-up tasks (p < 0.005). Moreover, four prediction models were developed based on the use of force plate data to estimate kinematics concerning humerus acceleration (p < 0.001). In conclusion, this research demonstrated that force plates are a valid tool for upper-limb assessment with significant correlations with dynamometer and 3D motion capture measures. Full article

(This article belongs to the Special Issue Novel Optical Biosensors in Biomechanics and Physiology)

► Show Figures

Figure 1

13 pages, 2231 KB

Open AccessArticle

Using Wearable MEG to Study the Neural Control of Human Stepping

by Meaghan E. Spedden, George C. O’Neill, Timothy O. West, Tim M. Tierney, Stephanie Mellor, Nicholas A. Alexander, Robert Seymour, Jesper Lundbye-Jensen, Jens Bo Nielsen, Simon F. Farmer, Sven Bestmann and Gareth R. Barnes

Sensors 2025, 25(13), 4160; https://doi.org/10.3390/s25134160 - 4 Jul 2025

Cited by 2 | Viewed by 3121

Abstract

A central challenge in movement neuroscience is developing methods for non-invasive spatiotemporal imaging of brain activity during natural, whole-body movement. We test the utility of a new brain imaging modality, optically pumped magnetoencephalography (OP-MEG), as an instrument to study the spatiotemporal dynamics of human walking. Specifically, we ask whether known physiological signals can be recovered during discrete steps involving large-scale, whole-body translation. Our findings show that by using OP-MEG, we can image the brain during large-scale, natural movements. We provide proof-of-principle evidence for movement-related changes in beta band activity during stepping vs. standing, which are source-localized to the sensorimotor cortex. This work supports the significant potential of the OP-MEG modality for addressing fundamental questions in human gait research relevant to both the physiological and pathological mechanisms of walking. Full article

(This article belongs to the Special Issue Novel Optical Biosensors in Biomechanics and Physiology)

► Show Figures

Journal Menu

Journal Browser

Novel Optical Biosensors in Biomechanics and Physiology

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI