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Use of Marker and Markerless Motion Capturing Technologies for Digital Human Modeling

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 4629

Special Issue Editors

Prof. Dr. Sofia Scataglini

E-Mail Website
Guest Editor
Department of Rehabilitation Sciences and Physiotherapy, Center for Health and Technology (CHaT), Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium
Interests: human factors and ergonomics; AI in healthcare; digital human modeling; smart health; biomechanics; neuroengineering; cognitive modelling; computer vision; standardization; integrated product development; design for all
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Steven Truijen

E-Mail Website
Guest Editor
Department of Rehabilitation Sciences and Physiotherapy, MOVANT, Faculty of Medicine and Health Sciences, University of Antwerp, 2610 Antwerp, Belgium
Interests: biomechanics; movement analysis; activity recognition

Special Issue Information

Dear Colleagues,

Marker-based and markerless systems are used for capturing subject motion during a motor task to create, or integrate and/or interact with, a digital human model.

Digital human modeling is the science of representing humans with their physical properties, characteristics, and behaviors in computerized, virtual models. Motion capture technologies (e.g., markers and markerless) can be used to create these models or to interact with and/or be integrated into those models.

Digital human models serve a wide range of practical purposes across various fields (medicine, health, space, automotive, industry, sport, and fashion, etc.) and applications.

Aspects of design and/or ergonomics evaluation and optimization, simulation and analysis, training, education, and/or gamification using motion capture technologies will be taken into consideration. Together with these aspects, modeling techniques and algorithms, combined with cutting-edge technologies, will be important assets for exploring innovation through this Special Issue.

By encompassing these aspects and promoting collaboration across diverse fields, this Special Issue has the potential to contribute significantly to the advancement of digital human modeling and its applications using motion capture technologies.

We aim to represent human characteristics and behaviors accurately. Furthermore, we hope that this Special Issue will drive innovation in areas such as anthropometry, design, biomechanics, ergonomics, simulation, training, and education across diverse fields and applications.

The exchange of knowledge and ideas across disciplines can lead to breakthroughs and discoveries that benefit research and society as a whole. By fostering this interdisciplinary approach, this Special Issue can serve as a valuable resource for researchers, practitioners, and professionals interested in the practical applications and future developments of digital human modeling using motion capture technologies.

Prof. Dr. Sofia Scataglini
Prof. Dr. Steven Truijen
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

  • digital human modeling
  • motion capture technologies
  • marker and markerless technologies
  • 3D and 4D scanning
  • wearable technologies
  • inertial measurement unit
  • anthropometry
  • biomechanics
  • design and ergonomics

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Published Papers (3 papers)

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Research

27 pages, 5687 KiB  
Article
Experimental Comparison between 4D Stereophotogrammetry and Inertial Measurement Unit Systems for Gait Spatiotemporal Parameters and Joint Kinematics
by Sara Meletani, Sofia Scataglini, Marco Mandolini, Lorenzo Scalise and Steven Truijen
Sensors 2024, 24(14), 4669; https://doi.org/10.3390/s24144669 - 18 Jul 2024
Cited by 1 | Viewed by 880
Abstract
(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with [...] Read more.
(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with an inertial motion capture system (Xsens) to evaluate their reliability and accuracy in assessing gait spatiotemporal parameters and joint kinematics. (2) Methods: This study included 13 healthy people and one hemiplegic patient, and it examined stance time, swing time, cycle time, and stride length. Statistical analysis included paired samples t-test, Bland–Altman plot, and the intraclass correlation coefficient (ICC). (3) Results: A high degree of agreement and no significant difference (p > 0.05) between the two measurement systems have been found for stance time, swing time, and cycle time. Evaluation of stride length shows a significant difference (p < 0.05) between Xsens and Move4D. The highest root-mean-square error (RMSE) was found in hip flexion/extension (RMSE = 10.99°); (4) Conclusions: The present work demonstrated that the system Move4D can estimate gait spatiotemporal parameters (gait phases duration and cycle time) and joint angles with reliability and accuracy comparable to Xsens. This study allows further innovative research using 4D (3D over time) scanning for quantitative gait assessment in clinical practice. Full article
(This article belongs to the Special Issue Use of Marker and Markerless Motion Capturing Technologies for Digital Human Modeling)
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25 pages, 10060 KiB  
Article
Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification
by Laura Alejandra Espitia-Mora, Manuel Andrés Vélez-Guerrero and Mauro Callejas-Cuervo
Sensors 2024, 24(11), 3371; https://doi.org/10.3390/s24113371 - 24 May 2024
Viewed by 1858
Abstract
Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents [...] Read more.
Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment. Full article
(This article belongs to the Special Issue Use of Marker and Markerless Motion Capturing Technologies for Digital Human Modeling)
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18 pages, 3900 KiB  
Article
A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation
by Florian Michaud, Francisco Mouzo, Daniel Dopico and Javier Cuadrado
Sensors 2024, 24(10), 3042; https://doi.org/10.3390/s24103042 - 10 May 2024
Cited by 1 | Viewed by 1327
Abstract
Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model’s predictive capabilities and its applicability to [...] Read more.
Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model’s predictive capabilities and its applicability to practical scenarios. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench to replicate a knee subject to a total knee replacement. The original knee inserts of the training station were replaced with personalized 3D-printed bones incorporating their corresponding implants, and multiple sensors with their respective supports were added. The recorded movement of the patella was used in combination with the forces recorded by the pressure sensor and the load cells, to validate the results obtained from the simulation, which was performed by means of a multibody dynamics formulation implemented in a custom-developed library. The utilization of 3D-printed models and sensors facilitated cost-effective and replicable experimental validation of computational simulations, thereby advancing orthopedic surgery while circumventing ethical concerns. Full article
(This article belongs to the Special Issue Use of Marker and Markerless Motion Capturing Technologies for Digital Human Modeling)
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