Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
The Application of Sensing Technologies for Gait Analysis and Rehabilitation:...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

The Application of Sensing Technologies for Gait Analysis and Rehabilitation: The Role of Digital Health and Artificial Intelligence

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 673

Special Issue Editor

Dr. Charles Odonkor

E-Mail Website
Guest Editor
Yale School of Medicine, Yale University, New Haven, CT 06510, USA
Interests: wearable devices that involve biosensors that, for a patient with a foot injury, for example, track changes in range of motion, gait speed, physical activity, and other measures of function

Special Issue Information

Dear Colleagues,

The integration of sensing technologies with artificial intelligence and digital health is transforming the landscape of gait analysis and rehabilitation. Traditional gait assessments, often limited to clinical settings and subjective observation, are increasingly being augmented—or replaced—by wearable sensors, ambient sensing systems, and data-driven models that offer objective, continuous, and ecologically valid insights into human movement.

This Special Issue brings together cutting-edge research that leverages digital sensing platforms—such as inertial measurement units (IMUs), pressure sensors, optical systems, and electromyography—paired with advanced computational approaches like machine learning and computer vision. These technologies enable precise gait characterization, remote monitoring, and personalized rehabilitation interventions across a variety of patient populations, including those with neurological, musculoskeletal, or age-related mobility impairments.

We invite original research articles, reviews, and technical reports for this Special Issue, exploring how sensing technologies, digital health platforms, and AI-powered analytics are revolutionizing gait analysis and rehabilitation.

Dr. Charles Odonkor
Guest Editor

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

  • gait analysis
  • wearable sensors
  • rehabilitation
  • artificial intelligence
  • digital health
  • machine learning
  • inertial measurement units (IMUs)
  • telemedicine
  • motion capture
  • smart healthcare

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 (1 paper)

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

Research

12 pages, 1163 KB  
Article
Sensor Input Type and Location Influence Outdoor Running Terrain Classification via Deep Learning Approaches
by Gabrielle Thibault, Philippe C. Dixon and David J. Pearsall
Sensors 2025, 25(19), 6203; https://doi.org/10.3390/s25196203 - 7 Oct 2025
Viewed by 474
Abstract
Background/Objective: Understanding the training effect in high-level running is important for performance optimization and injury prevention. This includes awareness of how different running surface types (e.g., hard versus soft) may modify biomechanics. Recent studies have demonstrated that deep learning algorithms, such as convolutional [...] Read more.
Background/Objective: Understanding the training effect in high-level running is important for performance optimization and injury prevention. This includes awareness of how different running surface types (e.g., hard versus soft) may modify biomechanics. Recent studies have demonstrated that deep learning algorithms, such as convolutional neural networks (CNNs), can accurately classify human activity collected via body-worn sensors. To date, no study has assessed optimal signal type, sensor location, and model architecture to classify running surfaces. This study aimed to determine which combination of signal type, sensor location, and CNN architecture would yield the highest accuracy in classifying grass and asphalt surfaces using inertial measurement unit (IMU) sensors. Methods: Running data were collected from forty participants (27.4 years + 7.8 SD, 10.5 ± 7.3 SD years of running) with a full-body IMU system (head, sternum, pelvis, upper legs, lower legs, feet, and arms) on grass and asphalt outdoor surfaces. Performance (accuracy) for signal type (acceleration and angular velocity), sensor configuration (full body, lower body, pelvis, and feet), and CNN model architecture was tested for this specific task. Moreover, the effect of preprocessing steps (separating into running cycles and amplitude normalization) and two different data splitting protocols (leave-n-subject-out and subject-dependent split) was evaluated. Results: In general, acceleration signals improved classification results compared to angular velocity (3.8%). Moreover, the foot sensor configuration had the best performance-to-number of sensor ratio (95.5% accuracy). Finally, separating trials into gait cycles and not normalizing the raw signals improved accuracy by approximately 28%. Conclusion: This analysis sheds light on the important parameters to consider when developing machine learning classifiers in the human activity recognition field. A surface classification tool could provide useful quantitative feedback to athletes and coaches in terms of running technique effort on varied terrain surfaces, improve training personalization, prevent injuries, and improve performance. Full article
(This article belongs to the Special Issue The Application of Sensing Technologies for Gait Analysis and Rehabilitation: The Role of Digital Health and Artificial Intelligence)
►▼ Show Figures

Figure 1

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