sensors-logo

Journal Browser

Journal Browser

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 2790

Special Issue Editors


E-Mail
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
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
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

E-Mail Website
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)

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 (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 637 KB  
Article
Influence of Sex and Body Size on the Validity of the Microsoft Kinect for Frontal Plane Knee Kinematics During Landings
by Jillian Neufeld, Vital Nwaokoro and Derek N. Pamukoff
Sensors 2025, 25(17), 5593; https://doi.org/10.3390/s25175593 - 8 Sep 2025
Viewed by 852
Abstract
Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. [...] Read more.
Three-dimensional (3D) motion capture is inaccessible, and the Microsoft Kinect is an alternative to measure surrogates of knee valgus that may contribute to anterior cruciate ligament (ACL) injury risk. We evaluated the influence of sex and body size on the agreement between methods. A total of 40 (10 per sex and BMI group) participants were included. The Kinect and motion capture measured knee ankle separation ratio (KASR) and knee abduction angles (KAAs). Intraclass correlation coefficients (ICCs) evaluated agreement between methods. 2 (sex) by 2 (BMI) by 2 (method) ANOVA compared kinematics between groups. Agreement between methods was moderate-to-good for KASR (initial contact ICCs 0.667–0.86; peak flexion ICCs 0.766–0.882). Agreement for KAA was low-to-moderate (initial contact ICCs 0.128–0.575; peak flexion ICCs 0.315–0.760). There was a BMI-by-method interaction for KASR at initial contact (p < 0.01) and a main effect of method (p < 0.01). There were BMI-by-method interactions for KAA (initial contact p > 0.01; peak knee flexion p < 0.01). The high BMI group had greater KAAs than the low BMI group, but only using motion capture. The Kinect is an alternative for measuring KASR, but not KAA. The high BMI group had greater KAAs than the low BMI group, but only when measured with motion capture. Full article
Show Figures

Figure 1

17 pages, 1243 KB  
Article
Biomechanical Effects of a Passive Lower-Limb Exoskeleton Designed for Half-Sitting Work Support on Walking
by Qian Li, Naoto Haraguchi, Bian Yoshimura, Sentong Wang, Makoto Yoshida and Kazunori Hase
Sensors 2025, 25(16), 4999; https://doi.org/10.3390/s25164999 - 12 Aug 2025
Viewed by 623
Abstract
The half-sitting posture is essential for many functional tasks performed by industrial workers. Thus, passive lower-limb exoskeletons, known as wearable chairs, are increasingly used to relieve lower-limb loading in such scenarios. However, although these devices lighten muscle effort during half-sitting tasks, they can [...] Read more.
The half-sitting posture is essential for many functional tasks performed by industrial workers. Thus, passive lower-limb exoskeletons, known as wearable chairs, are increasingly used to relieve lower-limb loading in such scenarios. However, although these devices lighten muscle effort during half-sitting tasks, they can disrupt walking mechanics and balance. Moreover, rigorous biomechanical data on joint moments and contact forces during walking with such a device remain scarce. Therefore, this study conducted a biomechanical evaluation of level walking with a wearable chair to quantify its effects on gait and joint loading. Participants performed walking experiments with and without the wearable chair. An optical motion capture system and force plates collected kinematic and ground reaction data. Six-axis force sensors measured contact forces and moments. These measurements were fed into a Newton–Euler inverse dynamics model to estimate lower-limb joint moments and assess joint loading. The contact measurements showed that nearly all rotational load was absorbed at the thigh attachment, while the ankle attachment served mainly as a positional guide with minimal moment transfer. The inverse dynamics analysis revealed that the wearable chair introduced unintended rotational stresses at lower-limb joints, potentially elevating musculoskeletal risk. This detailed biomechanical evidence underpins targeted design refinements to redistribute loads and better protect lower-limb joints. Full article
Show Figures

Figure 1

13 pages, 4603 KB  
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 589
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
Show Figures

Figure 1

Back to TopTop