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Human-Robot Interaction Applications in Internet of Things (IoT) Era

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 33825

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Special Issue Editors

Dr. Alexandros Tzallas

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Guest Editor

School of Applied Technology, Department of Computer Engineering, Technological Educational Institute of Epirus, Kostakioi, GR-47100 Arta, Greece
Interests: biomedical signal and image processing; EEG; wearable devices; computational intelligence; data modelling and decision support systems; biomedical engineering; medical physics
Special Issues, Collections and Topics in MDPI journals

Dr. Nikolaos Giannakeas

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Guest Editor

Assistant Professor, Department of Informatics & Telecommunications, School of Informatics & Telecommunications, University of Ioannina, Kostakioi, GR-47100 Arta, Greece
Interests: Biomedical Engineering; BioinformaticsImage & Signal Processing; Machine Learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to participate in a Special Issue on “Human–Robot Interaction Applications in the Internet of Things (IoT) Era”. This Special Issue will focus on the ongoing evolution of Internet of Things (IoT) together with the rapid dispersal of robots in many activities of daily life. Potential topics for this Special Issue may include, but are not limited to:

•      Ambient intelligence;
•      Smart environments (cities, transport, homes, farms, and health facilities);
•      Smart vehicles;
•      IoT for criminal activities;
•      Internet of medical things;
•      IoT for eHealth, elderly, and aging;
•      IoT sensors for smart eHealth devices;
•      Hybrid for Internet of Vehicles;
•      Robot ecology;
•      Brain–Computer interface applications in robotics control;
•      Building and home automation;
•      Drone–IoT integrated networks;
•      Cyber-physical systems;
•      Human–Robot interfaces;
•      Wireless body area and sensor networks;
•      Military IoT RFID radio frequency identification;
•      Mobile robotics with hybrid sensors and deep learning;
•      Low-power and lossy networks;
•      IoT-aided robotics applications and IoT-enabled flying ad hoc networks in smart agriculture;
•      Distributed artificial intelligence.

Tentative authors are invited to submit unpublished research, review, or short communication articles. The submitted articles should include novel applied research and pilot studies specifically targeting IoT-aided robotics applications.

Dr. Alexandros Tzallas
Dr. Nikolaos Giannakeas
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. 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 2600 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

Internet of Robotic Things
smart environments
brain–computer interfaces
artificial intelligence and robotics
autonomous sensor networks
smart wearable IoT devices
human–computer interaction
environmental monitoring and sensing
applied IoT data analytics
networked robotics

Published Papers (6 papers)

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Research

Jump to: Review

22 pages, 3749 KiB

Open AccessArticle

IoT Micro-Blockchain Fundamentals

by Aristidis G. Anagnostakis, Nikolaos Giannakeas, Markos G. Tsipouras, Euripidis Glavas and Alexandros T. Tzallas

Sensors 2021, 21(8), 2784; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082784 - 15 Apr 2021

Cited by 13 | Viewed by 3990

Abstract

In this paper we investigate the essential minimum functionality of the autonomous blockchain, and the minimum hardware and software required to support it in the micro-scale in the IoT world. The application of deep-blockchain operation in the lower-level activity of the IoT ecosystem, is expected to bring profound clarity and constitutes a unique challenge. Setting up and operating bit-level blockchain mechanisms on minimal IoT elements like smart switches and active sensors, mandates pushing blockchain engineering to the limits. “How deep can blockchain actually go?” “Which is the minimum Thing of the IoT world that can actually deliver autonomous blockchain functionality?” To answer, an experiment based on IoT micro-controllers was set. The “Witness Protocol” was defined to set the minimum essential micro-blockchain functionality. The protocol was developed and installed on a peer, ad-hoc, autonomous network of casual, real-life IoT micro-devices. The setup was tested, benchmarked, and evaluated in terms of computational needs, efficiency, and collective resistance against malicious attacks. The leading considerations are highlighted, and the results of the experiment are presented. Findings are intriguing and prove that fully autonomous, private micro-blockchain networks are absolutely feasible in the smart dust world, utilizing the capacities of the existing low-end IoT devices. Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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12 pages, 1749 KiB

Open AccessArticle

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

by Evangelos Antoniou, Pavlos Bozios, Vasileios Christou, Katerina D. Tzimourta, Konstantinos Kalafatakis, Markos G. Tsipouras, Nikolaos Giannakeas and Alexandros T. Tzallas

Sensors 2021, 21(7), 2339; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072339 - 27 Mar 2021

Cited by 39 | Viewed by 7254

Abstract

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology. Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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16 pages, 1677 KiB

Open AccessArticle

Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation

by Kyriakos Koritsoglou, Vasileios Christou, Georgios Ntritsos, Georgios Tsoumanis, Markos G. Tsipouras, Nikolaos Giannakeas and Alexandros T. Tzallas

Sensors 2020, 20(21), 6389; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216389 - 09 Nov 2020

Cited by 12 | Viewed by 3227

Abstract

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM). Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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16 pages, 1948 KiB

Open AccessArticle

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

by Mareike Daeglau, Frank Wallhoff, Stefan Debener, Ignatius Sapto Condro, Cornelia Kranczioch and Catharina Zich

Sensors 2020, 20(6), 1620; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061620 - 14 Mar 2020

Cited by 13 | Viewed by 4606

Abstract

Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors. Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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19 pages, 10839 KiB

Open AccessArticle

Towards IoT-Aided Human–Robot Interaction Using NEP and ROS: A Platform-Independent, Accessible and Distributed Approach

by Enrique Coronado and Gentiane Venture

Sensors 2020, 20(5), 1500; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051500 - 09 Mar 2020

Cited by 18 | Viewed by 5864

Abstract

This article presents the novel Python, C# and JavaScript libraries of Node Primitives (NEP), a high-level, open, distributed, and component-based framework designed to enable easy development of cross-platform software architectures. NEP is built on top of low-level, high-performance and robust sockets libraries (ZeroMQ and Nanomsg) and robot middlewares (ROS 1 and ROS 2). This enables platform-independent development of Human–Robot Interaction (HRI) software architectures. We show minimal code examples for enabling Publish/Subscribe communication between Internet of Things (IoT) and Robotics modules. Two user cases performed outside laboratories are briefly described in order to prove the technological feasibility of NEP for developing real-world applications. The first user case briefly shows the potential of using NEP for enabling the creation of End-User Development (EUD) interfaces for IoT-aided Human–Robot Interaction. The second user case briefly describes a software architecture integrating state-of-art sensory devices, deep learning perceptual modules, and a ROS -based humanoid robot to enable IoT-aided HRI in a public space. Finally, a comparative study showed better latency results of NEP over a popular state-of-art tool (ROS using rosbridge) for connecting different nodes executed in local-host and local area network (LAN). Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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Review

Jump to: Research

21 pages, 535 KiB

Open AccessReview

eHMI: Review and Guidelines for Deployment on Autonomous Vehicles

by Juan Carmona, Carlos Guindel, Fernando Garcia and Arturo de la Escalera

Sensors 2021, 21(9), 2912; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092912 - 21 Apr 2021

Cited by 34 | Viewed by 7649

Abstract

Human–machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human–machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems. Full article

(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)

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