Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier

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Abstract

Being able to recognize human activities is essential for several applications such as health monitoring, fall detection, context-aware mobile applications. In this work, we perform the recognition of the human activity based on the combined Weighted SVM and HMM by taking advantage of the relative strengths of these two classification paradigms. One significant advantage in WSVMs is that, they deal the problem of imbalanced data but his drawback is that, they are inherently static classifiers - they do not implicitly model temporal evolution of data. HMMs have the advantage of being able to handle dynamic data with certain assumptions about stationary and independence. The experiment results on real datasets show that the proposed method possess the better robustness and distinction.

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Most cited references 10

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Activity recognition using cell phone accelerometers

Jennifer Kwapisz, Gary M. Weiss, Samuel Moore (2011)

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A Survey of Online Activity Recognition Using Mobile Phones

Muhammad Shoaib, Stephan Bosch, Ozlem Incel … (2015)

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.

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Impact of smartphone’s on society

M Sarwar, T. Soomro, M SARWAR … (2013)

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Author and article information

Contributors

Mohamed Jmaiel: mohamed.jmaiel@redcad.org

Mounir Mokhtari: mounir.mokhtari@imt.fr

Bessam Abdulrazak: bessam.abdulrazak@usherbrooke.ca

Hamdi Aloulou: hamdi.aloulou@gmail.com

Slim Kallel: slim.kallel@fsegs.usf.tn

M’hamed Bilal Abidine: abidineb@hotmail.com

Journal

Journal ID (publisher-id): 978-3-030-51517-1

Journal ID (doi): 10.1007/978-3-030-51517-1

Journal ID (nlm-ta): The Impact of Digital Technologies on Public Health in Developed and Developing Countries

Title: The Impact of Digital Technologies on Public Health in Developed and Developing Countries

Subtitle: 18th International Conference, ICOST 2020, Hammamet, Tunisia, June 24–26, 2020, Proceedings

ISBN (Print): 978-3-030-51516-4

ISBN (Electronic): 978-3-030-51517-1

Publication date (Electronic): 31 May 2020

Publication date PMC-release: 31 May 2020

Volume: 12157

Pages: 386-394

Affiliations

[8 ]GRID grid.498575.2, Digital Research Centre of Sfax, ; Sfax, Tunisia

[9 ]GRID grid.4444.0, ISNI 0000 0001 2112 9282, Institut Mines-Télécom, CNRS, ; Paris, France

[10 ]GRID grid.86715.3d, ISNI 0000 0000 9064 6198, Université de Sherbrooke, ; Sherbrooke, QC Canada

[11 ]GRID grid.498575.2, Digital Research Centre of Sfax, ; Sfax, Tunisia

[12 ]GRID grid.412124.0, ISNI 0000 0001 2323 5644, University of Sfax, ; Sfax, Tunisia

GRID grid.420190.e, ISNI 0000 0001 2293 1293, Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants, LISIC Lab., Electronics and Computer Sciences Department, , University of Science and Technology Houari Boumediene (USTHB), ; Algiers, Algeria

Article

Publisher ID: 35

DOI: 10.1007/978-3-030-51517-1_35

PMC ID: 7313284

SO-VID: 41a399c0-c831-42d1-b3f0-1179646a180b

License:

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

History

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issue license Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Keywords: activity recognition,classification,weighted svm,hmm

Data availability:

Keywords: activity recognition, classification, weighted svm, hmm