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Authors: George Vavoulas 1 ; Charikleia Chatzaki 2 ; Thodoris Malliotakis 1 ; Matthew Pediaditis 2 and Manolis Tsiknakis 2

Affiliations: 1 Technological Educational Institute of Crete, Greece ; 2 Technological Educational Institute of Crete and Foundation for Research and Technology – Hellas, Greece

Keyword(s): Human Activity Recognition, Activities of Daily Living, Smartphone, Accelerometer, Dataset.

Abstract: The use of smartphones for human activity recognition has become popular due to the wide adoption of smartphones and their rich sensing features. This article introduces a benchmark dataset, the MobiAct dataset, for smartphone-based human activity recognition. It comprises data recorded from the accelerometer, gyroscope and orientation sensors of a smartphone for fifty subjects performing nine different types of Activities of Daily Living (ADLs) and fifty-four subjects simulating four different types of falls. This dataset is used to elaborate an optimized feature selection and classification scheme for the recognition of ADLs, using the accelerometer recordings. Special emphasis was placed on the selection of the most effective features from feature sets already validated in previously published studies. An important qualitative part of this investigation is the implementation of a comparative study for evaluating the proposed optimal feature set using both the MobiAct dataset and a nother popular dataset in the domain. The results obtained show a higher classification accuracy than previous reported studies, which exceeds 99% for the involved ADLs. (More)

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Paper citation in several formats:
Vavoulas, G.; Chatzaki, C.; Malliotakis, T.; Pediaditis, M. and Tsiknakis, M. (2016). The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones. In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AGEINGWELL 2016) - ICT4AWE; ISBN 978-989-758-180-9; ISSN 2184-4984, SciTePress, pages 143-151. DOI: 10.5220/0005792401430151

@conference{ict4awe16,
author={George Vavoulas. and Charikleia Chatzaki. and Thodoris Malliotakis. and Matthew Pediaditis. and Manolis Tsiknakis.},
title={The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones},
booktitle={Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AGEINGWELL 2016) - ICT4AWE},
year={2016},
pages={143-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005792401430151},
isbn={978-989-758-180-9},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AGEINGWELL 2016) - ICT4AWE
TI - The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones
SN - 978-989-758-180-9
IS - 2184-4984
AU - Vavoulas, G.
AU - Chatzaki, C.
AU - Malliotakis, T.
AU - Pediaditis, M.
AU - Tsiknakis, M.
PY - 2016
SP - 143
EP - 151
DO - 10.5220/0005792401430151
PB - SciTePress

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