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Authors: Bishoy Sefen 1 ; Sebastian Baumbach 2 ; Andreas Dengel 2 and Slim Abdennadher 1

Affiliations: 1 Germnan University in Cairo, Egypt ; 2 University of Kaiserslautern and German Research Center for Artificial Intelligence (DFKI), Germany

ISBN: 978-989-758-172-4

Keyword(s): Activity Recognition, Fitness Games, Sensors, Smartphones, Smartwatches, Machine Learning, Data Mining.

Related Ontology Subjects/Areas/Topics: Ambient Intelligence ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Unobtrusive and mobile activity monitoring using ubiquitous, cheap and widely available technology is the key requirement for human activity recognition supporting novel applications, such as health monitoring. With the recent progress in wearable technology, pervasive sensing and computing has become feasible. However, recognizing complex activities on light-weight devices is a challenging task. In this work, a platform to combine off-the-shelf sensors of smartphones and smartwatches for recognizing human activities in real-time is proposed. In order to achieve the best tradeoff between the system’s computational complexity and recognition accuracy, several evaluations were carried out to determine which classification algorithm and features to be used. Therefore, a data set from 16 participants was collected that includes normal daily activities and several fitness exercises. The analysis results showed that naive Bayes performs best in our experiment in both the accuracy and effici ency of classification, while the overall classification accuracy is 87% ± 2.4. (More)

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Paper citation in several formats:
Sefen, B.; Baumbach, S.; Dengel, A. and Abdennadher, S. (2016). Human Activity Recognition - Using Sensor Data of Smartphones and Smartwatches.In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 488-493. DOI: 10.5220/0005816004880493

@conference{icaart16,
author={Bishoy Sefen. and Sebastian Baumbach. and Andreas Dengel. and Slim Abdennadher.},
title={Human Activity Recognition - Using Sensor Data of Smartphones and Smartwatches},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={488-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005816004880493},
isbn={978-989-758-172-4},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Human Activity Recognition - Using Sensor Data of Smartphones and Smartwatches
SN - 978-989-758-172-4
AU - Sefen, B.
AU - Baumbach, S.
AU - Dengel, A.
AU - Abdennadher, S.
PY - 2016
SP - 488
EP - 493
DO - 10.5220/0005816004880493

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