A Survey of Online Activity Recognition Using Mobile Phones

M. Shoaib, S. Bosch, O. Durmaz, Johan Scholten, Paul J.M. Havinga

Research output: Contribution to journalArticle

  • 141 Citations

Abstract

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.
LanguageUndefined
Pages2059-2085
Number of pages27
JournalSensors (Switserland)
Volume15
Issue number1
DOIs
StatePublished - 19 Jan 2015

Keywords

  • EWI-25695
  • Real Time
  • online activity recognition
  • Survey
  • Smart phones
  • Accelerometer
  • mobile phone sensing
  • human activity recognition review
  • METIS-312494
  • IR-94631
  • mobile phone

Cite this

@article{49194522f249439dbb8fafdc675ea57d,
title = "A Survey of Online Activity Recognition Using Mobile Phones",
abstract = "Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.",
keywords = "EWI-25695, Real Time, online activity recognition, Survey, Smart phones, Accelerometer, mobile phone sensing, human activity recognition review, METIS-312494, IR-94631, mobile phone",
author = "M. Shoaib and S. Bosch and O. Durmaz and Johan Scholten and Havinga, {Paul J.M.}",
note = "eemcs-eprint-25695",
year = "2015",
month = "1",
day = "19",
doi = "10.3390/s150102059",
language = "Undefined",
volume = "15",
pages = "2059--2085",
journal = "Sensors (Switserland)",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "1",

}

A Survey of Online Activity Recognition Using Mobile Phones. / Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

In: Sensors (Switserland), Vol. 15, No. 1, 19.01.2015, p. 2059-2085.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Survey of Online Activity Recognition Using Mobile Phones

AU - Shoaib,M.

AU - Bosch,S.

AU - Durmaz,O.

AU - Scholten,Johan

AU - Havinga,Paul J.M.

N1 - eemcs-eprint-25695

PY - 2015/1/19

Y1 - 2015/1/19

N2 - Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

AB - Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

KW - EWI-25695

KW - Real Time

KW - online activity recognition

KW - Survey

KW - Smart phones

KW - Accelerometer

KW - mobile phone sensing

KW - human activity recognition review

KW - METIS-312494

KW - IR-94631

KW - mobile phone

U2 - 10.3390/s150102059

DO - 10.3390/s150102059

M3 - Article

VL - 15

SP - 2059

EP - 2085

JO - Sensors (Switserland)

T2 - Sensors (Switserland)

JF - Sensors (Switserland)

SN - 1424-8220

IS - 1

ER -