Special Issue "Design, Challenges and Applications of Healthcare Machinery, Device and Sensors"

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 1277

Special Issue Editors

College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Interests: robotics; e-health; control engineering
Dr. Imdad Ullah
E-Mail Website
Guest Editor
Department of Information Systems, College of Computer Engineering & Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
Interests: blockchain; IoT
Control and Energy Management Laboratory (CEM-Lab) National School of Engineers of Sfax, Sfax, Tunisia
Interests: mechatronics; human-robot interaction; medical robotics; exoskeleton robotics; machine learning; stroke rehabilitation; robot operating system (ROS); IoT; national instrument; sensors; deep learning; intelligent systems; robotics; modeling and simulation

Special Issue Information

Dear Colleagues,

The use of technology to improve healthcare has grown increasingly popular over the past decade. Governments around the world, in the public sector as well as the private sector, have put in place policies to provide technology-enabled health services. This process has been especially accelerated by the COVID-19 pandemic. Many innovative hardware/software solutions have been incorporated into healthcare, leading to significant discoveries and remarkable improvement in the this sector.

The goal of technology incorporation is to improve the quality of medical care and quality of life for people with disabilities, to solve health-related situations in more effective ways and settle problems that could not be solved prior to technological solutions.

Despite this success in integrating technological solutions into the health service, many technologies are not being used as much or as intended, and targeted efficiency goals are not always being achieved. In fact, the healthcare sector is facing many challenges in light of today’s digital transformation and the fourth industrial revolution, which have seen the incorporation of intelligence, big data and IoT for the further development of the healthcare system. It is clear that there is room for improvement in the development, implementation and evaluation of the healthcare system.

In this context, this Special Issue will focus on the design, challenges and applications of healthcare machinery, devices and sensors. Topics of interest include tools, equipment, sensors, machines, devices and all kinds of healthcare-related applications, as well as challenges currently facing healthcare systems.

Dr. Yassine Bouteraa
Dr. Imdad Ullah
Dr. Ben Abdallah Ismail
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. Machines is an international peer-reviewed open access monthly 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.


  • e-health
  • intelligent healthcare
  • IoT-based healthcare systems
  • assistive technology
  • wearable devices
  • robotics for medical applications

Published Papers (1 paper)

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


Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis
Machines 2023, 11(2), 235; https://doi.org/10.3390/machines11020235 - 05 Feb 2023
Viewed by 919
Intelligent lower-limb prosthesis appears in the public view due to its attractive and potential functions, which can help amputees restore mobility and return to normal life. To realize the natural transition of locomotion modes, locomotion mode classification is the top priority. There are [...] Read more.
Intelligent lower-limb prosthesis appears in the public view due to its attractive and potential functions, which can help amputees restore mobility and return to normal life. To realize the natural transition of locomotion modes, locomotion mode classification is the top priority. There are mainly five steady-state and periodic motions, including LW (level walking), SA (stair ascent), SD (stair descent), RA (ramp ascent), and RD (ramp descent), while ST (standing) can also be regarded as one locomotion mode (at the start or end of walking). This paper mainly proposes four novel features, including TPDS (thigh phase diagram shape), KAT (knee angle trajectory), CPO (center position offset) and GRFPV (ground reaction force peak value) and designs ST classifier and artificial neural network (ANN) classifier by using a user-dependent dataset to classify six locomotion modes. Gaussian distributions are applied in those features to simulate the uncertainty and change of human gaits. An angular velocity threshold and GRFPV feature are used in the ST classifier, and the artificial neural network (ANN) classifier explores the mapping relation between our features and the locomotion modes. The results show that the proposed method can reach a high accuracy of 99.16% ± 0.38%. The proposed method can provide accurate motion intent of amputees to the controller and greatly improve the safety performance of intelligent lower-limb prostheses. The simple structure of ANN applied in this paper makes adaptive online learning algorithms possible in the future. Full article
Show Figures

Figure 1

Back to TopTop