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Electronics
Special Issues
Applications of Computer-Assisted Technologies in Sports Injuries and Rehabilitation
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Applications of Computer-Assisted Technologies in Sports Injuries and Rehabilitation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 9 June 2025 | Viewed by 1522

Special Issue Editors

Prof. Dr. Bin Liu

E-Mail Website
Guest Editor
International School of Information Science & Engineering, Dalian University of Technology, Dalian 116024, China
Interests: medical 3D reconstruction; medical image processing; computer vision; preoperative planning and simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianxin Zhang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China
Interests: computer vision; medical image analysis; intelligent computing

Special Issue Information

Dear Colleagues,

Knee ligament injuries are one of the most common injuries during physical activity. Ligaments are important structures that maintain the stability of the knee joint. Ligament injuries can have a significant impact on a patient's motor function. The patient cannot run, jump, or make emergency stops. Ligament reconstruction is designed to restore the biomechanical state of the knee joint, thereby allowing the patient to resume physiological movement, minimizing the risk of the meniscus and articular cartilage damage, and delaying the progression of post-traumatic knee osteoarthritis. This is a classic computer-assisted surgical technique that has received extensive attention in engineering and medicine. In actual clinical treatment, to restore the mechanical properties of the knee joint, accurate positioning is the key to ligament reconstruction. In addition, computer-aided preoperative planning can also reduce operative time. In this case, obtaining the biological geometric information of the knee joint, 3D reconstruction of the ligament, precise positioning of the ligament, and intelligent processing of CT/MRI images are all quite challenging. Therefore, the development of computer-assisted accurate knee joint ligament repair is an indivisible part of resolving the accurate joint injuries surgery issue in the future. The purpose of this special issue is to promote outstanding research concerning all aspects in the realm of computer-assisted accurate orthopedic surgery technologies, focusing on state-of-the-art progress, developments, and new trends. Potential topics include but are not limited to the following:

  • Learning-based ligament segmentation in CT/MRI images;
  • Computer-assisted rheumatoid arthritis surgery;
  • Computer-assisted bone pain preoperative planning;
  • Transfer learning approach in lesion Recognition;
  • Knee joint registration of MRI bone model and CT bone model;
  • Computer-assisted preoperative localization of ligament;
  • Anterior cruciate ligament (ACL) coronal view injury diagnosis system;
  • Double-bundle versus single-bundle reconstruction for ligament rupture;
  • Dynamic research on spatial lengths of knee cruciate ligaments;
  • Ligament repair planning based on VR/AR technology;
  • Near-infrared optical positioning system for ligament surgery;
  • Minimally invasive and personalized treatment for knee prosthesis replacement.

Technical Program Committee Members：

Dr. Qifeng Wang
E-mail: 
International School of Information Science & Engineering, Dalian University of Technology, Dalian 116024, China

Dr. Xiangjun Yang
E-mail: International School of Information Science & Engineering, Dalian University of Technology, Dalian 116024, China

Prof. Dr. Bin Liu
Prof. Dr. Jianxin Zhang
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. Electronics 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

  • computer-assisted rheumatoid arthritis surgery
  • bone pain preoperative planning
  • VR/AR technology
  • MRI bone model and CT bone model
  • transfer learning approach

Published Papers (1 paper)

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Research

18 pages, 3271 KiB  
Article
Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring
by Evanthia Faliagka, Vasileios Skarmintzos, Christos Panagiotou, Vasileios Syrimpeis, Christos P. Antonopoulos and Nikolaos Voros
Electronics 2023, 12(16), 3375; https://doi.org/10.3390/electronics12163375 - 8 Aug 2023
Cited by 2 | Viewed by 1060
Abstract
In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical [...] Read more.
In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel. Full article
(This article belongs to the Special Issue Applications of Computer-Assisted Technologies in Sports Injuries and Rehabilitation)
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