Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 37647

Share This Special Issue

Special Issue Editors

Prof. Dr. Redha Taiar

E-Mail Website
Guest Editor

Department of Physical Education and Sports (EPS), University of Reims Champagne-Ardenne, 51100 Reims, France
Interests: biomechanics of health disease and rehabilitation; industry engineering for medicine and high-level sport
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mario Bernardo-Filho

E-Mail Website
Guest Editor

Institute of Biology Roberto Alcantara Gomes, Rio de Janeiro State University, Rio de Janeiro, Brazil
Interests: integrative and complementary medicine (auriculotherapy and acupuncture), mechanical vibrations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) will enter into all spheres of life like indispensable helper of human in routine activities. AI is the Engineering of the Future and will brings benefits to humans in his daily life by the utilization of technology such as computer and algorithm to facilitate human in day today routine work as well as in advance assignments. She can assist human beings in many and can improve the efficiency in different cases supporting and enhancing Human activities.Virtual reality (VR) is emerging freshly in the field of interdisciplinary research. VR technology is getting supreme using computer hardware, software and virtual environment technology through which the real world can be simulated dynamically. VR technology can be put into application in different case such as sports training simulation and medicine. The purpose of this special issue is to quantify the state of progress in terms of the use of artificial intelligence and virtual reality in biomechanics. Modelling, simulation, Artificial Intelligence, Virtual reality and Computing in musculoskeletal system permit to quantify and improve the discriminate parameters characterizing movement in different cases such as sport level, work and patients daily lives.

Prof. Redha Taiar
Prof. Mario Bernardo-Filho
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. Applied Sciences 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 2400 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

Augmented human
Artificial Intelligence, Virtual Reality
Mixed Reality Simulation
Computing
Healthcare
Wearable Technologies
musculoskeletal system
biological problems, modelization and simulation, daily life

Published Papers (9 papers)

Download All Papers

Research

Jump to: Review

12 pages, 2006 KiB

Open AccessArticle

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data

by Frederick W. Damen, David T. Newton, Guang Lin and Craig J. Goergen

Appl. Sci. 2021, 11(4), 1690; https://doi.org/10.3390/app11041690 - 13 Feb 2021

Cited by 9 | Viewed by 3364

Abstract

Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R² values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

7 pages, 939 KiB

Open AccessCommunication

Postural Control Disturbances Induced by Virtual Reality in Stroke Patients

by Charles Morizio, Maxime Billot, Jean-Christophe Daviet, Stéphane Baudry, Christophe Barbanchon, Maxence Compagnat and Anaick Perrochon

Appl. Sci. 2021, 11(4), 1510; https://doi.org/10.3390/app11041510 - 8 Feb 2021

Cited by 3 | Viewed by 2173

Abstract

People who survive a stroke are often left with long-term neurologic deficits that induce, among other impairments, balance disorders. While virtual reality (VR) is growing in popularity for postural control rehabilitation in post-stroke patients, studies on the effect of challenging virtual environments, simulating common daily situations on postural control in post-stroke patients, are scarce. This study is a first step to document the postural response of stroke patients to different challenging virtual environments. Five subacute stroke patients and fifteen age-matched healthy adults were included. All participants underwent posturographic tests in control conditions (open and closed eyes) and virtual environment without (one static condition) and with avatars (four dynamic conditions) using a head-mounted device for VR. In dynamic environments, we modulated the density of the virtual crowd (dense and light crowd) and the avoidance space with the avatars (near or far). Center of pressure velocity was collected by trial throughout randomized 30-s periods. Results showed that more challenging conditions (dynamic condition) induced greater postural disturbances in stroke patients than in healthy counterparts. Our study suggests that virtual reality environments should be adjusted in light of obtaining more or less challenging conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

19 pages, 8361 KiB

Open AccessArticle

Augmented Reality Based Surgical Navigation of Complex Pelvic Osteotomies—A Feasibility Study on Cadavers

by Joëlle Ackermann, Florentin Liebmann, Armando Hoch, Jess G. Snedeker, Mazda Farshad, Stefan Rahm, Patrick O. Zingg and Philipp Fürnstahl

Appl. Sci. 2021, 11(3), 1228; https://doi.org/10.3390/app11031228 - 29 Jan 2021

Cited by 18 | Viewed by 3377

Abstract

Augmented reality (AR)-based surgical navigation may offer new possibilities for safe and accurate surgical execution of complex osteotomies. In this study we investigated the feasibility of navigating the periacetabular osteotomy of Ganz (PAO), known as one of the most complex orthopedic interventions, on two cadaveric pelves under realistic operating room conditions. Preoperative planning was conducted on computed tomography (CT)-reconstructed 3D models using an in-house developed software, which allowed creating cutting plane objects for planning of the osteotomies and reorientation of the acetabular fragment. An AR application was developed comprising point-based registration, motion compensation and guidance for osteotomies as well as fragment reorientation. Navigation accuracy was evaluated on CT-reconstructed 3D models, resulting in an error of 10.8 mm for osteotomy starting points and 5.4° for osteotomy directions. The reorientation errors were 6.7°, 7.0° and 0.9° for the x-, y- and z-axis, respectively. Average postoperative error of LCE angle was 4.5°. Our study demonstrated that the AR-based execution of complex osteotomies is feasible. Fragment realignment navigation needs further improvement, although it is more accurate than the state of the art in PAO surgery. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

17 pages, 287 KiB

Open AccessArticle

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

by Alessandro Silacci, Redha Taiar and Maurizio Caon

Appl. Sci. 2021, 11(1), 313; https://doi.org/10.3390/app11010313 - 30 Dec 2020

Cited by 3 | Viewed by 3307

Abstract

In a world where the data is a central piece, we provide a novel technique to design training plans for road cyclists. This study exposes an in-depth review of a virtual coach based on state-of-the-art artificial intelligence techniques to schedule road cycling training sessions. Together with a dozen of road cycling participants’ training data, we were able to create and verify an e-coach dedicated to any level of road cyclists. The system can provide near-human coaching advice on the training of cycling athletes based on their past capabilities. In this case study, we extend the tests of our empirical research project and analyze the results provided by experts. Results of the conducted experiments show that the computational intelligence of our system can compete with human coaches at training planification. In this case study, we evaluate the system we previously developed and provide new insights and paths of amelioration for systems based on artificial intelligence for athletes. We observe that our system performs equal or better than the control training plans in 14 and 24 week training periods where it was evaluated as better in 4 of our 5 test components. We also report a higher statistical difference in the results of the experts’ evaluations between the control and virtual coach training plan (24 weeks; training load: X² = 4.751; resting time quantity: X² = 3.040; resting time distance: X² = 2.550; efficiency: X² = 2.142). Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

11 pages, 1712 KiB

Open AccessFeature PaperArticle

Aerobic Exercise with Superimposed Virtual Reality Improves Cognitive Flexibility and Selective Attention in Young Males

by Borja Sañudo, Ellie Abdi, Mario Bernardo-Filho and Redha Taiar

Appl. Sci. 2020, 10(22), 8029; https://doi.org/10.3390/app10228029 - 12 Nov 2020

Cited by 6 | Viewed by 2931

Abstract

The literature to date is limited regarding the implantation of VR in healthy young individuals with a focus on cognitive function. Thirty healthy males aged between 22.8 and 24.3 years volunteered to participate in the study randomly and were assigned to one of two groups with alike exercises: an experimental group (GE, n = 15) that performed an exercise protocol with a VR game and a controlled group that performed the exercise protocol without the VR (CON, n = 15). A 128-card computerized version of the Wisconsin Card Sorting Task (WCST) and the Stroop test were completed before and after the exercise protocol. There was a significant interaction effect between time and condition for WCST preservation errors (F_1,30 = 4.59, p = 0.041, η²_p = 0.141) and a significant time effect for all WCST and Stroop outcomes in GE. Results of preliminary findings suggest that the use of a VR platform offers effective benefits with respect to cognitive flexibility and selective attention. In addition, participants can achieve additional benefits in cognitive flexibility by engaging in a traditional exercise protocol of a similar volume. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

12 pages, 900 KiB

Open AccessArticle

Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

by Richard Hughes

Appl. Sci. 2020, 10(14), 4834; https://doi.org/10.3390/app10144834 - 14 Jul 2020

Viewed by 2014

Abstract

Background: Biomechanists are often asked to provide expert opinions in legal proceedings, especially personal injury cases. This often involves using deterministic analysis methods, although the expert is expected to opine using a civil standard of “more likely than not” that is inherently probabilistic. Methods: A method is proposed for converting a class of deterministic biomechanical models into hybrid Bayesian networks that produce a probability well suited for addressing the civil standard of proof. The method was developed for spinal injury during lifting. Its generalizability was assessed by applying it to slip and fall events based on the coefficients of friction at the shoe–floor interface. Results: The proposed method is shown to be generalizable beyond lifting by applying it to a slip and fall event. Both the lifting and slip and fall models showed that incorporating evidence of injury could change the probabilities of critical quantities exceeding a threshold from “less likely than not” to “more likely than not.” Conclusions: The present work shows that it is possible to develop Bayesian networks for legal use based on laws of engineering mechanics and probabilistic descriptions of measurement error and human variability. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

14 pages, 2138 KiB

Open AccessArticle

Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence

by Wei Sun, Jiang Wang, Nan Zhang and Shuangming Yang

Appl. Sci. 2020, 10(8), 2857; https://doi.org/10.3390/app10082857 - 20 Apr 2020

Cited by 10 | Viewed by 3152

Abstract

In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

24 pages, 1827 KiB

Open AccessArticle

Simulation of Skeletal Muscles in Real-Time with Parallel Computing in GPU

by Octavio Navarro-Hinojosa and Moisés Alencastre-Miranda

Appl. Sci. 2020, 10(6), 2099; https://doi.org/10.3390/app10062099 - 20 Mar 2020

Cited by 3 | Viewed by 3697

Abstract

Modeling and simulation of the skeletal muscles are usually solved using the Finite Element method (FEM) which, although accurate, commonly needs a complex mesh and the solution is not processed in real-time. In this work, a meshfree model that simulates skeletal muscles considering their functioning and control based on electrical activity, their structure based on biological tissue, and that computes in real-time, is presented. Meshfree methods were used because they are able to surpass most of the limitations that are present in mesh-based methods. The muscular belly was modelled as a particle-based viscoelastic fluid, which is controlled using the monodomain model and shape matching. The smoothed particle hydrodynamics (SPH) method was used to solve both the fluid dynamics and the electrophysiological model. To analyze the accuracy of the method, a similar model was implemented with FEM. Both FEM and SPH methods provide similar solutions of the models in terms of pressure and displacement, with an error of around 0.09, with up to a 10% difference between them. Through the use of General-purpose computing on graphics processing units (GPGPU), real-time simulations that offer a viable alternative to mesh-based models for interactive biological tissue simulations was achieved. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures

Figure 1

Review

Jump to: Research

15 pages, 1036 KiB

Open AccessReview

Trends in the Characterization of the Proximal Humerus in Biomechanical Studies: A Review

by Angel D. Castro-Franco, Ismael Mendoza-Muñoz, Álvaro González-Ángeles, Samantha E. Cruz-Sotelo, Ana Maria Castañeda and Miriam Siqueiros-Hernández

Appl. Sci. 2020, 10(18), 6514; https://doi.org/10.3390/app10186514 - 18 Sep 2020

Cited by 4 | Viewed by 11318

Abstract

Proximal humerus fractures are becoming more common due to the aging of the population, and more related scientific research is also emerging. Biomechanical studies attempt to optimize treatments, taking into consideration the factors involved, to obtain the best possible treatment scenario. To achieve this, the use of finite element analysis (FEA) is necessary, to experiment with situations that are difficult to replicate, and which are sometimes unethical. Furthermore, low costs and time requirements make FEA the perfect choice for biomechanical studies. Part of the complete process of an FEA involves three-dimensional (3D) bone modeling, mechanical properties assignment, and meshing the bone model to be analyzed. Due to the lack of standardization for bone modeling, properties assignment, and the meshing processes, this article aims to review the most widely used techniques to model the proximal humerus bone, according to its anatomy, for FEA. This study also seeks to understand the knowledge and bias behind mechanical properties assignment for bone, and the similarities/differences in mesh properties used in previous FEA studies of the proximal humerus. The best ways to achieve these processes, according to the evidence, will be analyzed and discussed, seeking to obtain the most accurate results for FEA simulations. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Virtual Reality (VR) in Biomechanics)

► Show Figures