Special Issue "Data from Smartphone and Wearables"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Joaquín Torres-Sospedra
E-Mail Website
Guest Editor
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localisation and positioning
Special Issues and Collections in MDPI journals
Dr. Aleksandr Ometov
E-Mail Website
Guest Editor
Tampere University, 33720 Tampere, Finland
Interests: wireless communications; information security

Special Issue Information

Dear colleagues,

The emerging market of wearables is growing due to the presence of the state-of-the-art devices in our lives. Wristbands that track our sport activity, smartwatches with localization capabilities, and eHealth devices that monitor our vital constants are becoming more common in the 21st century life. The ecosystem of interconnected wearables is a source of crowdsourced information that enables advanced big data analysis and the application of deep learning models.

This Special Issue is devoted but not limited to data sets including any raw data collected by wearable devices and smartphones, as well as processing of such kind of data for wireless communications, tracking, indoor and outdoor positioning, eHealth monitoring, sport analysis, and gesture recognition, among others.

Dr. Joaquín Torres-Sospedra
Dr. Ometov Aleksandr
Guest Editor

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. Data is an international peer-reviewed open access quarterly 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 1000 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

  • indoor positioning
  • tracking

Published Papers (1 paper)

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Open AccessData Descriptor
SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions
Data 2020, 5(1), 7; https://doi.org/10.3390/data5010007 - 14 Jan 2020
Abstract
Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for [...] Read more.
Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for human-aware navigation datasets challenging. Firstly, the problem itself is subjective, different dataset contributors will very frequently disagree to some extent on their labels. Secondly, the number of variables to consider is undetermined culture-dependent. This paper presents SocNav1, a dataset for social navigation conventions. SocNav1 aims at evaluating the robots’ ability to assess the level of discomfort that their presence might generate among humans. The 9280 samples in SocNav1 seem to be enough for machine learning purposes given the relatively small size of the data structures describing the scenarios. Furthermore, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as graph neural networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the nature of the dataset, an analysis and validation of the collected data are also presented. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
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