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Sensor-Based Methods for Kinematics, Kinetics, and Physiology in Motion: Applications and Metrological Characterization

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 474

Special Issue Editors


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Guest Editor
Department of Engineering, University of Niccolò Cusano, 00166 Rome, Italy
Interests: biomechanics; wearable sensors; gait analysis; posturography; falling; rehabilitation; robotics; machine learning; EMG; measurements
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Guest Editor
Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
Interests: wearable sensors; sensors; physiological monitoring; algorithms for data processing including machine learning; applications of sensors in clinical, occupational, and sports fields
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health (DSMC), University of Brescia, Viale Europa 11, 25123 Brescia, Italy
Interests: mechanical systems and applications; electrical measurements; biomechanics and wearable robots

Special Issue Information

Dear Colleagues,

The assessment of kinematics, kinetics, and physiological parameters is fundamental for understanding human movement and its applications in clinical practice, sports science, and robotics. In healthcare, objective motion analysis supports the diagnosis and monitoring of neuromuscular disorders, enabling more effective rehabilitation strategies. In sports, accurate performance evaluation enhances training programs and injury prevention. In robotics, data on human movement inform the development of humanoids and assistive devices. Additionally, physiological signals—such as respiratory rate, heart rate, and muscle activation—provide valuable complementary insights, enabling a more integrated understanding of biomechanics.

Sensor-based systems, whether wearable or non-wearable, have transformed the way kinematic, kinetic, and physiological variables are measured, offering quantitative, repeatable, and standardized alternatives to subjective and observational assessments. However, ensuring the reliability and accuracy of these systems across different contexts of use requires rigorous methodologies for validation, calibration, and metrological characterization. The integration of sensor fusion techniques, advanced data processing, and robust measurement protocols plays a crucial role in improving precision and reproducibility.

While optoelectronic motion capture systems and force platforms continue to represent the gold standard for biomechanical evaluation, the ongoing development of both wearable technologies and non-wearable sensing systems has made it possible to conduct accurate assessments outside controlled laboratory settings. These advancements are expanding the scope of biomechanical applications, allowing for continuous, context-aware monitoring in clinical rehabilitation, sports performance, ergonomics, and robotic-assisted interventions.

This Special Issue invites contributions focused on the use of sensor-based systems for the measurement and analysis of kinematic, kinetic, and physiological variables, with particular attention to methods, methodological frameworks, and accuracy evaluation. Topics of interest include, but are not limited to, the following:

  • Development and validation of innovative sensor-based methods for motion and physiological analysis;
  • Wearable, portable, and non-wearable sensing technologies for biomechanics applications;
  • Accuracy evaluation, calibration, and standardization of sensor-based measurements;
  • Metrological approaches to ensure measurement traceability, repeatability, and reliability;
  • Sensor fusion and multimodal data integration for biomechanical and physiological monitoring;
  • Advanced data analysis methods, including AI and machine learning for sensor data interpretation;
  • Real-time feedback systems and adaptive technologies in clinical, sports, or robotic contexts;
  • Non-invasive physiological monitoring integrated with motion analysis (e.g., respiratory rate, heart rate, EMG).

By fostering contributions focused on methodological rigor, accuracy, and validation, this Special Issue aims to enhance the scientific robustness and translational impact of sensor-based technologies in the study of biomechanics and physiological monitoring.

Dr. Ilaria Mileti
Dr. Juri Taborri
Dr. Carlo Massaroni
Dr. Marco Ghidelli
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 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

  • development and validation of innovative sensor-based methods for motion and physiological analysis
  • wearable, portable, and non-wearable sensing technologies for biomechanics applications
  • accuracy evaluation, calibration, and standardization of sensor-based measurements
  • metrological approaches to ensure measurement traceability, repeatability, and reliability
  • sensor fusion and multimodal data integration for biomechanical and physiological monitoring
  • advanced data analysis methods, including ai and machine learning for sensor data interpretation
  • real-time feedback systems and adaptive technologies in clinical, sports, or robotic contexts
  • non-invasive physiological monitoring integrated with motion analysis (e.g., respiratory rate, heart rate, EMG)

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Published Papers (1 paper)

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Research

13 pages, 4603 KiB  
Article
Verification of Footwear Effects on a Foot Deformation Approach for Estimating Ground Reaction Forces and Moments
by Naoto Haraguchi, Hajime Ohtsu, Bian Yoshimura and Kazunori Hase
Sensors 2025, 25(12), 3705; https://doi.org/10.3390/s25123705 - 13 Jun 2025
Viewed by 314
Abstract
The foot deformation approach (FDA) estimates the ground reaction force (GRF) and moment (GRM) from kinematic data with practical accuracy, low computational cost, and no requirement for training data. Our previous study demonstrated practical estimation accuracy of the FDA under barefoot conditions. However, [...] Read more.
The foot deformation approach (FDA) estimates the ground reaction force (GRF) and moment (GRM) from kinematic data with practical accuracy, low computational cost, and no requirement for training data. Our previous study demonstrated practical estimation accuracy of the FDA under barefoot conditions. However, since the FDA estimates GRFs and GRMs based on foot deformation under body weight, there are concerns about its applicability to footwear conditions, where the foot deformation characteristics differ from those of bare feet. Following the issue, this study conducted a walking experiment at three different speeds with running shoes and sneakers to investigate the impact of footwear on GRF prediction using the FDA. The results showed that the FDA successfully provided practical accuracy when shoes were worn, comparable to that for a barefoot participant. The FDA offers advantages for estimating GRFs and GRMs for the footwear condition, while eliminating the need for collecting training data and enabling rapid analysis and feedback in clinical settings. Although the FDA cannot fully eliminate the effects of footwear and movement speed on prediction accuracy, it has the potential to serve as a convenient biomechanical-based method for estimating GRFs and GRMs during sports and daily activities with footwear. Full article
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