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Special Issue "Assistance Robotics and Biosensors 2019"

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

Deadline for manuscript submissions: 20 June 2019

Special Issue Editors

Guest Editor
Prof. Dr. Andrés Ubeda

Automatics, Robotics and Computer Vision Group, University of Alicante, Alicante, Spain
Website | E-Mail
Interests: neuromuscular mechanisms of motor control; neurorehabilitation procedures; human-machine interaction; assistive technologies; neurorobotics; myoelectric control; brain–computer interfaces
Guest Editor
Prof. Dr. Fernando Torres Medina

University of Alicante
Website | E-Mail
Phone: +34-965909491
Interests: robotics; visual servoing; intelligent robotics manipulation; mobile robots; education
Guest Editor
Prof. Dr. Santiago Puente

Automatics, Robotics and Computer Vision Group, University of Alicante, Alicante, Spain
Website | E-Mail
Interests: robotics and automation; automatic disassembling; advanced automation; intelligent manipulation; new trends in robotics

Special Issue Information

Dear Colleagues,

In recent years, the use of robotics to help motor-disabled people has experienced a significant growth, mostly based on the development and improvement of biosensor technology and the increasing interest in solving accessibility and rehabilitation limitations in a more natural and effective way. For that purpose, biomedical signal processing has been combined with robotic technology, such as exoskeletons or assistive robotic arms or hands. However, efforts are still needed to make these technologies affordable and useful for end users, as current biomedical devices are still mostly present in rehabilitation centers, hospitals and research facilities.

This Special Issue is focused on breakthrough developments in the field of assistive and rehabilitation robotics, including current scientific progress in biomedical signal processing, robotic manipulation and grasping, mobile robotics, exoskeletons and prosthetics. Papers should address innovative solutions in these fields. Both review articles and original research papers are solicited.

Prof. Dr. Andrés Ubeda
Prof. Dr. Fernando Torres
Prof. Dr. Santiago Puente
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

  • electromyographic (EMG) sensors
  • electroencephalographic (EEG) sensors
  • robotic manipulation and grasping in assistive environments
  • mobile robotics in assistive environments
  • advanced biomedical signal processing in rehabilitation and assistance
  • robotic exoskeletons
  • robotic hands and prostheses

Published Papers (2 papers)

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Research

Open AccessArticle
Directional Forgetting for Stable Co-Adaptation in Myoelectric Control
Sensors 2019, 19(9), 2203; https://doi.org/10.3390/s19092203
Received: 12 April 2019 / Revised: 1 May 2019 / Accepted: 8 May 2019 / Published: 13 May 2019
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Abstract
Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. [...] Read more.
Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms. Full article
(This article belongs to the Special Issue Assistance Robotics and Biosensors 2019)
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Open AccessArticle
Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor
Sensors 2019, 19(2), 371; https://doi.org/10.3390/s19020371
Received: 28 November 2018 / Revised: 11 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
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Abstract
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This [...] Read more.
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures. Full article
(This article belongs to the Special Issue Assistance Robotics and Biosensors 2019)
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