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Emerging Sensing Techniques for Human–Computer Interaction Systems/ Human–Machine Interfaces

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6354

Special Issue Editors


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Guest Editor
Faculty of Information Technology, Brno University of Technology, Božetěchova 2, 612 00 Brno, Czech Republic

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Guest Editor
Warwick Manufacturing Group, The University of Warwick, Coventry, UK

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Guest Editor
Faculty of Information Technology, Brno University of Technology, Božetěchova 2, 612 00 Brno, Czech Republic

Special Issue Information

Dear Colleagues,

Human computer interaction systems are becoming increasingly important in the contemporary world. The computer systems, both traditional computers and mobile communication devices, become an important part of everyday human life, both personal and professional, and smooth interaction is essential for their efficient use. Moreover, devices based on computer systems, such as home appliances, measurement devices but also cyberphysical systems and robotic technology involve interaction with humans and often require or benefit from novel interaction methods. Human computer interaction systems or, in wider sense human machine interfaces, inevitable involve sensors and to address importance of this fact, this special issue of Sensors calls for original papers that describe new research and/or innovative approaches in human computer interaction, interfaces, and, of course, sensors involved, including physical, optical, MEMS/NEMS, and other sensor principles as well as smart/intelligent sensors including those implemented in embedded systems, and sensor networks approaches involving localization and tracking or computer vision. We are looking forward to your participation in this special issue.

Prof. Dr. Pavel Zemčík
Prof. Dr. Alan Chalmers
Dr. Vítězslav Beran
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

  • human–computer interaction systems
  • human–machine interfaces
  • interaction and sensors
  • vistual and augmented reality
  • robotic interfaces
  • novel interaction methods
  • smart sensors and interaction
  • computer vision and interaction

Published Papers (2 papers)

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Research

20 pages, 2954 KiB  
Article
No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing
by Nuwan T. Attygalle, Luis A. Leiva, Matjaž Kljun, Christian Sandor, Alexander Plopski, Hirokazu Kato and Klen Čopič Pucihar
Sensors 2021, 21(17), 5771; https://doi.org/10.3390/s21175771 - 27 Aug 2021
Cited by 9 | Viewed by 2437
Abstract
Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational [...] Read more.
Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor. Full article
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10 pages, 944 KiB  
Communication
Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots
by Stanley Y. B. Huang, Chih-Jen Lee and Shih-Chin Lee
Sensors 2021, 21(17), 5687; https://doi.org/10.3390/s21175687 - 24 Aug 2021
Cited by 6 | Viewed by 3441
Abstract
With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user’s response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user’s response mechanism towards financial technology chatbots [...] Read more.
With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user’s response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user’s response mechanism towards financial technology chatbots has been relatively under-investigated. To fill these literature gaps, latent growth curve modeling was adopted by the present research to survey Taiwanese users of fintech chatbots. The present study proposed a customer continuance model to predict continuance intention for fintech chatbots and that cognitive and emotional dimensions positively influence the growth in a user’s attitude toward fintech chatbots, which in turn, positively influences continuance intention over time. In total, 401 customers of fintech chatbots were surveyed through three time points to examine the relationship between these variables over six months. The results support the theoretical model of this research and can advance the literature of fintech chatbots and the information technology adoption model. Full article
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