Sensors

Journal Browser

► Journal Browser

State-of-the-Art Sensor Technology in Human Movement Analysis

Share This Special Issue

Editors

Dr. Adam J. Toth

E-Mail Website
Guest Editor

Physical Education and Sport Sciences Department, Faculty of Education and Health Sciences, University of Limerick, V94 T9PX Limerick, Ireland
Interests: neurostimulation; sensory–motor learning; neuroscience; biomechanics; esports science

Prof. Dr. Ross Anderson

E-Mail Website
Co-Guest Editor

Department of Physical Education & Sport Sciences, University of Limerick, V94 T9PX Limerick, Ireland
Interests: 3D motion analysis; data collection; biomechanics; kinematics; motion analysis; movement analysis; gait analysis; human movement; motion capture; sport biomechanics

Dr. Sarah Kraeutner

E-Mail Website
Co-Guest Editor

Department of Psychology, Irving K. Barber Faculty of Arts and Social Sciences, University of British Columbia, Kelowna, BC V1V 1V7, Canada
Interests: motor learning; motor imagery; stroke; neuroimaging; transcranial magnetic stimulation

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advances in sensor technologies and their applications in human movement analysis. The integration of wearable, optical, and environmental sensors has revolutionized the measurement of biomechanics, motor control, and performance across clinical, sports, and everyday settings. We seek contributions that highlight innovative sensor designs, multimodal data fusion, novel signal processing and machine learning approaches, and validation studies demonstrating accuracy and reliability in real-world conditions. Papers exploring translational applications—from rehabilitation and ergonomics to skill acquisition and human–machine interaction—are particularly encouraged. Both original research and comprehensive reviews are welcome. Through this collection, we aim to showcase cutting-edge methodologies and foster interdisciplinary dialogue on how emerging sensor technologies are shaping the future of human movement science.

Dr. Adam J. Toth
Guest Editor

Prof. Dr. Ross Anderson
Dr. Sarah Kraeutner
Co-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-anonymized 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.

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 (2 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, 2537 KB

Open AccessArticle

A Proof-of-Concept Framework for Upper-Limb Segment Definition and Joint Angle Computation Using Marker-Based Motion Capture

by Catarina M. Amaro, Hannah Rice, Maria António Castro, Rui Mendes and Beatriz B. Gomes

Sensors 2026, 26(16), 5047; https://doi.org/10.3390/s26165047 - 9 Aug 2026

Viewed by 169

Abstract

Marker-based motion capture systems are widely used to estimate joint kinematics, yet their accuracy depends strongly on how anatomical segments and coordinate systems are defined. This proof-of-concept study aimed to describe and technically evaluate a structured and reproducible framework for upper-limb segment definition and joint-angle computation. Reflective markers were placed on anatomical landmarks of the trunk and upper limbs, and joint angles were computed using custom-developed MATLAB R2022b (MathWorks, Natick, MA, USA) routines. Baseline-corrected model-derived joint angles were compared with composite reference measurements obtained using a universal manual goniometer and a twin-axis biosignalsplux goniometer under predefined static conditions in two adult participants. Side-specific mean absolute error values ranged from 0.80° to 5.24°, while root mean square error values ranged from 0.86° to 5.24°. The largest discrepancies were observed during maximum wrist extension and left maximum radial deviation. The evaluated static observations showed close correspondence in several joint positions, although larger discrepancies occurred in selected end-range wrist positions. Given the limited sample, single recordings, and controlled static conditions, these findings should be interpreted as an initial demonstration of technical feasibility rather than evidence of generalisable validity or repeatability. The explicit framework provides a basis for further evaluation using larger samples, repeated marker applications, repeated trials, and dynamic multi-planar upper-limb tasks. Full article

(This article belongs to the Special Issue State-of-the-Art Sensor Technology in Human Movement Analysis)

► Show Figures

Figure 1

26 pages, 6622 KB

Open AccessArticle

Wearable IMU-Derived Kinematic Reference Profiles of Lower-Limb Kick and Wipe Gestures for Contactless Automotive Tailgate Activation

by János Dreveton, Moritz Labetzsch, Tim Gocke and Torsten Bertram

Sensors 2026, 26(14), 4469; https://doi.org/10.3390/s26144469 - 14 Jul 2026

Viewed by 473

Abstract

Contactless automotive tailgate activation relies on recognizing intentional lower-limb gestures near the rear bumper, yet these systems are developed and evaluated from sensor-specific recordings rather than from the underlying human movement, so quantitative, sensor-independent kinematic reference profiles for these gestures are lacking. This study establishes wearable inertial measurement unit (IMU)-derived reference profiles of two tailgate-activation gestures: a forward kick and a lateral wipe. Lower-body motion was recorded in 56 adult participants using a seven-sensor Xsens Awinda configuration under application-oriented conditions, yielding 6879 segmented movements. To the best of our knowledge, this is among the most extensive of such datasets, providing a sensor-independent, joint- and segment-level movement reference. Both gestures shared a common sagittal structure dominated by knee, ankle, and hip flexion/extension, with mean knee flexion/extension of

{45.9}^{\circ}

for kick and

{42.3}^{\circ}

for wipe movements. Wipe gestures differed through markedly larger non-sagittal components, with hip abduction/adduction of

{17.2}^{\circ}

versus

{7.7}^{\circ}

and ankle internal/external rotation of

{16.6}^{\circ}

versus

{8.7}^{\circ}

, confirmed in every participant (

p < 0.001

). Foot-segment kinematics showed the highest velocities, with a mean resultant foot velocity of approximately

1.8 m / s

. These profiles provide a quantitative biomechanical basis for benchmarking gesture-recognition sensor systems, informing detection-window and threshold selection, and enabling standardized, repeatable testing of contactless automotive HMI systems. Full article

(This article belongs to the Special Issue State-of-the-Art Sensor Technology in Human Movement Analysis)

► Show Figures

Journal Menu

Journal Browser

State-of-the-Art Sensor Technology in Human Movement Analysis

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI