Special Issue "Data from Smartphone and Wearables"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (15 January 2021).

Special Issue Editors

Dr. Joaquín Torres-Sospedra
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
Website
Guest Editor
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere‎, Finland
Interests: wireless communications; information security; authentication; distributed systems; blockchain; resource-constrained devices; wearable technology
Special Issues and Collections in MDPI journals

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 monthly 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 1400 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

  • Big Data
  • Biometric datasets
  • Body Area Networks
  • Crowdsourced data
  • Data analysis
  • Data Mining
  • Data processing
  • Data Profiling
  • Dataset comparisons
  • Dataset qualities
  • eHealth datasets
  • Healthcare data
  • Indoor Positioning
  • Location tracking Measurements datasets
  • Medical datasets
  • Open datasets
  • Personal Area Networks
  • Positioning datasets
  • Security datasets
  • Social data
  • Wearables
 

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Other

Open AccessData Descriptor
BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed
Data 2020, 5(4), 115; https://doi.org/10.3390/data5040115 - 07 Dec 2020
Abstract
To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of [...] Read more.
To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of the gait speed may be key to enable early detection of cognitive diseases such as dementia or Alzheimer’s disease. Wearable technologies can provide innovative solutions for healthcare problems. Bluetooth Low Energy (BLE) technology is excellent for wearables because it is very energy efficient, secure, and inexpensive. In this paper, the BLE-GSpeed database is presented. The dataset is composed of several BLE RSSI measurements obtained while users were walking at a constant speed along a corridor. Moreover, a set of experiments using a baseline algorithm to estimate the gait speed are also presented to provide baseline results to the research community. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults
Data 2020, 5(4), 91; https://doi.org/10.3390/data5040091 - 25 Sep 2020
Cited by 1
Abstract
Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the [...] Read more.
Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the literature that provide psycho-physiological data. The Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset presented in this paper provides 24 h of continuous psycho-physiological data, that is, inter-beat intervals data, heart rate data, wrist accelerometry data, sleep quality index, physical activity (i.e., number of steps per second), psychological characteristics (e.g., anxiety status, stressful events, and emotion declaration), and sleep hormone levels for 22 participants. The MMASH dataset will enable the investigation of possible relationships between the physical and psychological characteristics of people in daily life. Data were validated through different analyses that showed their compatibility with the literature. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments
Data 2020, 5(3), 67; https://doi.org/10.3390/data5030067 - 30 Jul 2020
Cited by 2
Abstract
The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but [...] Read more.
The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
Measurements of Mobile Blockchain Execution Impact on Smartphone Battery
Data 2020, 5(3), 66; https://doi.org/10.3390/data5030066 - 30 Jul 2020
Cited by 1
Abstract
This is a data descriptor paper for a set of the battery output data measurements during the turned on display discharge process caused by the execution of modern mobile blockchain projects on Android devices. The measurements were executed for Proof-of-Work (PoW) and Proof-of-Activity [...] Read more.
This is a data descriptor paper for a set of the battery output data measurements during the turned on display discharge process caused by the execution of modern mobile blockchain projects on Android devices. The measurements were executed for Proof-of-Work (PoW) and Proof-of-Activity (PoA) consensus algorithms. In this descriptor, we give examples of Samsung Galaxy S9 operation while a broader range of measurements is available in the dataset. Examples provide the data about battery output current, output voltage, temperature, and status. We also show the measurements obtained utilizing short-range (IEEE 802.11n) and cellular (LTE) networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the dataset’s nature, an analysis of the collected data is also briefly presented. This dataset may be of interest to both researchers from information security and human–computer interaction fields and industrial distributed ledger/blockchain developers. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
A Database for the Radio Frequency Fingerprinting of Bluetooth Devices
Data 2020, 5(2), 55; https://doi.org/10.3390/data5020055 - 21 Jun 2020
Abstract
Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order [...] Read more.
Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
Player Heart Rate Responses and Pony External Load Measures during 16-Goal Polo
Data 2020, 5(2), 34; https://doi.org/10.3390/data5020034 - 02 Apr 2020
Cited by 1
Abstract
This dataset provides information pertaining to the spatiotemporal stresses experienced by Polo ponies in play and the cardiovascular responses to these demands by Polo players, during 16-goal Polo. Data were collected by player-worn GPS units and paired heart rate monitors, across a New [...] Read more.
This dataset provides information pertaining to the spatiotemporal stresses experienced by Polo ponies in play and the cardiovascular responses to these demands by Polo players, during 16-goal Polo. Data were collected by player-worn GPS units and paired heart rate monitors, across a New Zealand Polo season. The dataset comprises observations from 160 chukkas of Open Polo, and is presented as per chukka per game (curated) and in per effort per player (raw) formats. Data for distance, speed, and high intensity metrics are presented and are further categorised into five equine-based speed zones, in accordance with previous literature. The purpose of this dataset is to provide a detailed quantification of the load experienced by Polo players and their ponies at the highest domestic performance level in New Zealand, as well as advancing the scope of previous Polo literature that has employed GPS or heart rate monitoring technologies. This dataset may be of interest to equine scientists and trainers, veterinary practitioners, and sports scientists. An exemplar template is provided to facilitate the adoption of this data collection approach by other practitioners. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

Open AccessData Descriptor
Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator
Data 2020, 5(1), 18; https://doi.org/10.3390/data5010018 - 17 Feb 2020
Cited by 1
Abstract
This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification [...] Read more.
This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification based on front-end hardware characteristics at transmitter or receiver side. Examples are given in this paper of achievable classification accuracy of six of the collected signal classes. The RFF is one of the state-of-the-art, promising methods to identify GNSS transmitters and receivers, and can find future applicability in anti-spoofing and anti-jamming solutions for example. The uses of the provided raw data are not limited to RFF studies, but can extend to uses such as testing GNSS acquisition and tracking, antenna array experiments, and so forth. Full article
(This article belongs to the Special Issue Data from Smartphone and Wearables)
Show Figures

Figure 1

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
Cited by 2
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)
Show Figures

Figure 1

Back to TopTop