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Special Issue "Multimodal Sensing for Understanding Behavior and Personality"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Grzegorz J. Nalepa
Website
Guest Editor
Institute of Applied Computer Science, Jagiellonian Univeristy, Krakow, Poland
Interests: artificial intelligence; knowledge engineering; affective computing; explainability
Prof. Dr. Marcin Grzegorzek
Website
Guest Editor
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
Interests: pattern recognition; medical informatics
Prof. Dr. Jose M. Juarez
Website
Guest Editor
Computer Science Faculty, Universidad de Murcia, 30100 Murcia, Spain
Interests: medical informatics; artificial intelligence; clinical decision support systems
Prof. Dr. John F. Rauthmann
Website
Guest Editor
Department of Psychology, University of Lübeck, Maria-Goeppert Straße 9a, D-23562 Lübeck, Germany
Interests: personality psychology; individual differences; personality computing; sensing of psychological signals

Special Issue Information

Dear Colleagues,

Sensors are everywhere. By the early 2020s, their number will have already exceeded one trillion. This development changes our society, e.g., the rapid innovation in wearable technology led to a societal phenomenon called Quantified Self (QS), a community of people who use the capabilities of technical devices to gain a profound understanding of collected self-related data. This huge amount of personal data generated every day may lead to a significant improvement of the accuracy of artificial intelligence (AI) methods, including machine learning and pattern recognition algorithms.

Understanding human behavior is crucial to personalized systems’ services for a wide variety of scenarios. However, a holistic assessment of human behavior and personality requires a proper combination of different methods. Multimodal sensing and context-driven information fusion is the key to allow systems to provide their services in the most suitable and efficient manner.

With this Special Issue, we would like to attract novel and original scientific contributions describing the newest research achievements in the area of sensor-based behavior and personality understanding using a wide range of AI methods, including pattern recognition and machine learning algorithms. We welcome both technical papers and submissions with a holistic vision, considering a more humanistic point of view. Finally, we encourage emphasis on relevant application domains of multimodal behavior and personality sensing.

Prof. Dr. Grzegorz J. Nalepa
Prof. Dr. Marcin Grzegorzek
Prof. Dr. Jose M. Juarez
Prof. Dr. John F. Rauthmann
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 2000 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

  • Multimodal sensing
  • Social sensing
  • Context-awareness
  • Information fusion
  • Personality
  • Quantified self
  • Individual differences
  • Ambient assisted living
  • Smart ambient spaces
  • Personalized healthcare
  • Physical activity assessment
  • Sensor-based sleep assessment
  • Human gait analysis and assessment
  • Physiotherapy assistance

Published Papers (2 papers)

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Research

Open AccessArticle
Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure
Sensors 2020, 20(11), 3312; https://doi.org/10.3390/s20113312 - 10 Jun 2020
Abstract
Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, [...] Read more.
Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints’ locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints’ locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method. Full article
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
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Open AccessArticle
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
Sensors 2020, 20(9), 2740; https://doi.org/10.3390/s20092740 - 11 May 2020
Abstract
Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to themselves while interacting with the [...] Read more.
Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to themselves while interacting with the system. The SDS is supposed to determine whether it is being addressed or not. All existing studies on acoustic H-M AD were conducted on corpora designed in such a way that a human addressee and a machine played different dialogue roles. This peculiarity influences speakers’ behaviour and increases vocal differences between human- and machine-directed utterances. In the present study, we consider the Restaurant Booking Corpus (RBC) that consists of complexity-identical human- and machine-directed phone calls and allows us to eliminate most of the factors influencing speakers’ behaviour implicitly. The only remaining factor is the speakers’ explicit awareness of their interlocutor (technical system or human being). Although complexity-identical H-M AD is essentially more challenging than the classical one, we managed to achieve significant improvements using data augmentation (unweighted average recall (UAR) = 0.628) over native listeners (UAR = 0.596) and a baseline classifier presented by the RBC developers (UAR = 0.539). Full article
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
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