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Cited by 153Context-Aware Activity Recognition SystemsIoT and Edge/Fog ComputingWireless Body Area Networks

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios

Open access
De Arriba-Pérez, Francisco, Caeiro-Rodríguez, Manuel, Santos-Gago, Juan·2016·Sensors
DOI 10.3390/s16091538PMC PMC5038811
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Abstract

Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains.

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Crossref · 10.3390/s16091538

Cites

42 of 42 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Wrist wearable devices’ 2015 market share on the left side, 1Q2016 market share on the right side, both by IDC Research Inc. (Framingham, MA, USA) (Adapted from [30,31]).
Figure 1 Wrist wearable devices’ 2015 market share on the left side, 1Q2016 market share on the right side, both by IDC Research Inc. (Framingham, MA, USA) (Adapted from [30,31]).CC BY · PubMed Central
Figure 2 2016 smartwatch OS prediction in the wrist wearable sector by IDC Research Inc. Adapted from [32].
Figure 2 2016 smartwatch OS prediction in the wrist wearable sector by IDC Research Inc. Adapted from [32].CC BY · PubMed Central
Figure 3 Sensors available in wearables.
Figure 3 Sensors available in wearables.CC BY · PubMed Central
Figure 4 Systems involved in the smartwatch data collection scenario.
Figure 4 Systems involved in the smartwatch data collection scenario.CC BY · PubMed Central
Figure 5 System representation for the wearable data transfer—indirect access.
Figure 5 System representation for the wearable data transfer—indirect access.CC BY · PubMed Central
Figure 6 System representation for the warehouse data transfer—direct access.
Figure 6 System representation for the warehouse data transfer—direct access.CC BY · PubMed Central
Figure 7 System representation for the warehouse data transfer—indirect access.
Figure 7 System representation for the warehouse data transfer—indirect access.CC BY · PubMed Central
Figure 8 System representation for the wearable data transfer—direct access.
Figure 8 System representation for the wearable data transfer—direct access.CC BY · PubMed Central
Figure 9 Sleep analysis by Fitbit based on data from the Fitbit Surge.
Figure 9 Sleep analysis by Fitbit based on data from the Fitbit Surge.CC BY · PubMed Central
Figure 10 Sleep analysis by Microsoft through the Microsoft Band.
Figure 10 Sleep analysis by Microsoft through the Microsoft Band.CC BY · PubMed Central
Figure 11 Sleep analysis by Jawbone through the Jawbone Up move.
Figure 11 Sleep analysis by Jawbone through the Jawbone Up move.CC BY · PubMed Central
Figure 12 Architecture of our analytic engine.
Figure 12 Architecture of our analytic engine.CC BY · PubMed Central
Figure 13 Sleep segments in Microsoft.
Figure 13 Sleep segments in Microsoft.CC BY · PubMed Central
Figure 14 Sleep segments in Fitbit. We only can detect two states of sleep in Fitbit wearables.
Figure 14 Sleep segments in Fitbit. We only can detect two states of sleep in Fitbit wearables.CC BY · PubMed Central
Figure 15 Sleep segments in Jawbone.
Figure 15 Sleep segments in Jawbone.CC BY · PubMed Central
Figure 16 Analytics location models.
Figure 16 Analytics location models.CC BY · PubMed Central
Figure 17 Adapted representation of the IoT Reference Model by Cisco System, Inc. [66].
Figure 17 Adapted representation of the IoT Reference Model by Cisco System, Inc. [66].CC BY · PubMed Central

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Citations

Cites

Show all 42 references

Research works printed but not linked: 3

  1. Mattern, F., and Floerkemeier, C. (2010). From Active Data Management to Event-Based Systems and More, Springer.
  2. IEEE-SA P2413. Standard for an Architectural Framework for the Internet of Things (IoT). Available online: https://standards.ieee.org/develop/project/2413.html.
  3. Mark, H., Ian, W., and Eibe, F. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers.

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Metadata

Authors
De Arriba-Pérez, F., Caeiro-Rodríguez, M., & Santos-Gago, J.
Affiliations
  • Universidade de Vigo (ES)
  • Department of Telematics Engineering, University of Vigo, Campus Lagoas-Marcosende, Vigo 36310, Spain
Corresponding author
De Arriba-Pérez, Francisco
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2016-09-21
Volume
16
Issue
9
Pages
1538
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s16091538
PMC
PMC5038811
OpenAlex
W2523343665
Semantic Scholar
25fb7987917007358083b5e826ad57c75f5f4694
Cited by
153
References
42
Citations by year
2026: 10 · 2025: 12 · 2024: 16 · 2023: 13 · 2022: 12 · 2021: 26 · 2020: 23 · 2019: 25 · 2018: 12 · 2017: 4
FWCI
9.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Investigo Institutional repository of UVigo (Universidade de Vigo) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Xunta de Galicia (GRC2013-006)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
Keywords
wearable sensor data, interoperability, heterogeneous devices, sleep monitoring, stress symptoms, heart rate, accelerometer, pedometers, machine-to-machine communication, data collection and processing

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