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Special Issue "Sensors and Machine Learning Methods Applied to Human-Computer Interaction"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Francisco Gomez-Donoso
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Guest Editor
University Institute for Computer Research, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
Interests: deep learning; 3D data; human–computer interaction; social robotics
Prof. Dr. Miguel Angel Cazorla Quevedo
E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
Interests: localization; mapping; 3D data; machine learning; robotics
Special Issues and Collections in MDPI journals
Dr. Félix Escalona Moncholí
E-Mail
Guest Editor
University Institute for Computer Research, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
Interests: machine learning; deep learning; SLAM; human–computer interaction; wearables
Dr. Sergio Orts-Escolano
E-Mail Website
Guest Editor
University Institute for Computer Research, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
Interests: 3D sensors; deep learning; depth estimation; calibration
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in machine learning, deep learning techniques, and sensors are greatly impacting how humans and computers and robots interact. For instance, surface electromyography sensors combined with deep-learning-based algorithms are currently being used to operate robotic prosthetic limbs or 3D pose estimation methods to control an avatar in Virtual Reality. Thus, the combination of sensors and machine learning techniques is enabling a range of novel and interesting applications.

This Special Issue is intended to cover cutting-edge applications and research on new sensors, machine learning methods or their combination to perform human–computer and human–robot interaction. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.

Dr. Francisco Gomez-Donoso
Prof. Miguel Quevedo
Dr. Félix Escalona Moncholí
Dr. Sergio Orts-Escolano

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 2200 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

  • Traditional machine learning algorithms for HCI and HRI 
  • Deep learning methods for HCI and HRI 
  • Reinforcement learning techniques for HCI and HRI 
  • Sensors for HCI and HRI 
  • Wearables for HCI and HRI 
  • Localization, mapping, and SLAM for HCI and HRI 
  • Sensor fusion for HCI and HRI

Published Papers (2 papers)

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Research

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Open AccessArticle
Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning
Sensors 2021, 21(8), 2589; https://doi.org/10.3390/s21082589 - 07 Apr 2021
Viewed by 347
Abstract
The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy [...] Read more.
The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy level of possibly slower, exact algorithmic solutions to the same problems. To this end, the existing simplex architecture for safety assurance in critical systems is extended by an adaptation mechanism, in which one of the redundant controllers (called a high-performance controller) is represented by a trained machine learning model. This model is retrained using field data to reduce its failure rate and redeployed continuously. The proposed adaptive simplex architecture (ASA) is evaluated on the basis of a robot path planning application with dynamic obstacle avoidance in the context of two human-robot collaboration scenarios in manufacturing. The evaluation results indicate that ASA enables a response by the robot in real time when it encounters an obstacle. The solution predicted by the model is economic in terms of path length and smoother than analogous algorithmic solutions. ASA ensures safety by providing an acceptance test, which checks whether the predicted path crosses the obstacle; in which case a suboptimal, yet safe, solution is used. Full article
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Review

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Open AccessReview
A Survey of Alzheimer’s Disease Early Diagnosis Methods for Cognitive Assessment
Sensors 2020, 20(24), 7292; https://doi.org/10.3390/s20247292 - 18 Dec 2020
Cited by 2 | Viewed by 737
Abstract
Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A high rate of deaths and high cost for detection, treatments, and patients care count amongst its consequences. Although there is no cure for dementia, a timely diagnosis helps [...] Read more.
Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A high rate of deaths and high cost for detection, treatments, and patients care count amongst its consequences. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support, appropriate medication, and maintenance, as far as possible, of engagement in intellectual, social, and physical activities. The early detection of Alzheimer Disease (AD) is considered to be of high importance for improving the quality of life of patients and their families. In particular, Virtual Reality (VR) is an expanding tool that can be used in order to assess cognitive abilities while navigating through a Virtual Environment (VE). The paper summarises common AD screening and diagnosis techniques focusing on the latest approaches that are based on Virtual Environments, behaviour analysis, and emotions recognition, aiming to provide more reliable and non-invasive diagnostics at home or in a clinical environment. Furthermore, different AD diagnosis evaluation methods and metrics are presented and discussed together with an overview of the different datasets. Full article
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