The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

George Vavoulas, Charikleia Chatzaki, Thodoris Malliotakis, Matthew Pediaditis, Manolis Tsiknakis


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 another popular dataset in the domain. The results obtained show a higher classification accuracy than previous reported studies, which exceeds 99% for the involved ADLs.


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Paper Citation

in Harvard Style

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 - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016) ISBN 978-989-758-180-9, pages 143-151. DOI: 10.5220/0005792401430151

in Bibtex Style

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 - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)},

in EndNote Style

JO - Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)
TI - The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones
SN - 978-989-758-180-9
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