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 2181

Special Issue Editors


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Guest Editor
College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Interests: robotics; e-health; control engineering

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Guest Editor
Department of Information Systems, College of Computer Engineering & Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
Interests: blockchain; IoT

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Guest Editor
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

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Keywords

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

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Published Papers (1 paper)

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Research

15 pages, 9571 KiB  
Article
Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis
by Yi Liu, Honglei An, Hongxu Ma and Qing Wei
Machines 2023, 11(2), 235; https://doi.org/10.3390/machines11020235 - 5 Feb 2023
Cited by 1 | Viewed by 1658
Abstract
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
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