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Special Issue "Sensors and Wearable Assistive Devices"

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

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Prof. Dr. Rezaul Begg

Leader: Gait and Intelligent Technologies Research Group, Chair: Program in Assistive Technologies Innovation (PATI), Institute for Health and Sport (IHeS), Victoria University, Melbourne, Australia
Website | E-Mail
Phone: +61 3 9919 1116
Interests: Assistive Devices; Exoskeletons; Gait Biomechanics; Machine Learning; Sensor Technology
Guest Editor
Dr. Kurt Mudie

Defence Science and Technology Group (DST Group), Australia
E-Mail
Interests: Assistive Devices; Exoskeletons; Gait Biomechanics; Load Carriage; Movement Variability
Guest Editor
Dr. Dan Billing

Defence Science and Technology Group (DST Group), Australia
E-Mail
Interests: Assistive Devices; Exoskeletons; Biomechanics; Load Carriage
Guest Editor
Dr. Daniel Lai

Smart Electronics Systems Research Group, College of Engineering and Science, Victoria University, Melbourne, Australia
Website | E-Mail
Interests: new sensing, communication technologies and computational intelligence for applications in health and sports

Special Issue Information

Dear Colleagues,

Assistive devices are designed to facilitate fundamental human actions, such as walking, grasping, lifting and carrying to improve productivity and reduce fatigue and musculoskeletal injuries. Developments in sensors, actuators, machine learning and related technology provide increasing potential for integrating humans with machines; reflected in a worldwide research effort to develop lightweight, low-cost, powered and unpowered assistive devices, such as exoskeletons. As innovations in artificial intelligence and sensor technology converge, there is also the capacity for designing increasingly intelligent or autonomous assistive technologies across medical, industrial and military applications. We invite submissions to this Special Issue with a focus on technology applications to wearable assistive devices. Topics of interest to our readers may include; sensors, actuators, exoskeletons, human-machine interaction, biomechanical and physiological data modelling and advanced computional methods, such as machine learning.

Prof. Rezaul Begg
Dr. Kurt Mudie
Dr. Dan Billing
Dr. Daniel Lai
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 papers will be 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 1800 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

  • assistive devices 
  • exoskeletons 
  • sensors 
  • machine learning 
  • biomechanical modelling 
  • computational methods 
  • human–machine interaction

Published Papers (2 papers)

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Research

Open AccessArticle
A New Integrated System for Assistance in Communicating with and Telemonitoring Severely Disabled Patients
Sensors 2019, 19(9), 2026; https://doi.org/10.3390/s19092026
Received: 5 March 2019 / Revised: 18 April 2019 / Accepted: 24 April 2019 / Published: 30 April 2019
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Abstract
In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication [...] Read more.
In this paper, we present a new complex electronic system for facilitating communication with severely disabled patients and telemonitoring their physiological parameters. The proposed assistive system includes three subsystems (Patient, Server, and Caretaker) connected to each other via the Internet. The two-way communication function is based on keywords technology using a WEB application implemented at the server level, and the application is accessed remotely from the patient’s laptop/tablet PC. The patient’s needs can be detected by using different switch-type sensors that are adapted to the patient’s physical condition or by using eye-tracking interfaces. The telemonitoring function is based on a wearable wireless sensor network, organized around the Internet of Things concept, and the sensors acquire different physiological parameters of the patients according to their needs. The mobile Caretaker device is represented by a Smartphone, which uses an Android application for communicating with patients and performing real-time monitoring of their physiological parameters. The prototype of the proposed assistive system was tested in “Dr. C.I. Parhon” Clinical Hospital of Iaşi, Romania, on hospitalized patients from the Clinic of Geriatrics and Gerontology. The system contributes to an increase in the level of care and treatment for disabled patients, and this ultimately lowers costs in the healthcare system. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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Open AccessArticle
Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton
Sensors 2019, 19(4), 963; https://doi.org/10.3390/s19040963
Received: 31 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
PDF Full-text (2548 KB) | HTML Full-text | XML Full-text
Abstract
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize [...] Read more.
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users’ movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class “no lift”) were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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