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

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

2016

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 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.

References

  1. Anguita, D., Ghio, A., Oneto, L., Parra, X. and ReyesOrtiz, J. L., 2012, 'Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine', in Ambient Assisted Living and Home Care, Springer Berlin Heidelberg, VitoriaGasteiz.
  2. Anjum, A. and Ilyas, M. U., 2013, 'Activity recognition using smartphone sensors', Consumer Communications and Networking Conference (CCNC), 11-14 January 2013, pp. 914-919, DOI: 10.1109/CCNC.2013.6488584.
  3. Bayat, A., Pomplun, M. and Tran, D. A., 2014, 'A Study on Human Activity Recognition Using Accelerometer Data from Smartphones', 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14), 2014, pp. 450-457, DOI:10.1016/j.procs.2014.07.009.
  4. Buber, E. and Guvensan, A. M., 2014, 'Discriminative time-domain features for activity recognition on a mobile phone', IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 21-24 April 2014, pp. 1-6, DOI:10.1109/ISSNIP.2014.6827651.
  5. Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L. and Cook, D. J., 2012, 'Simple and Complex Activity Recognition through Smart Phones', 8th International Conference on Intelligent Environments (IE), 26-29 June 2012, pp. 214-221.
  6. Fan, L., Wang, Z. and Wang, H., 2013, 'Human Activity Recognition Model Based on Decision Tree', Proceedings of the 2013 International Conference on Advanced Cloud and Big Data (CBD 7813), 2013, pp. 64-68, DOI: 10.1109/CBD.2013.19.
  7. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H., 2009, 'The WEKA data mining software: an update', ACM SIGKDD Explorations Newsletter, vol 11, no. 1, pp. 10-18.
  8. Khan, A. M., Lee, Y. K., Lee, S. Y. and Kim, T. S., 2010, 'Human Activity Recognition via an AccelerometerEnabled-Smartphone Using Kernel Discriminant Analysis', 5th International Conference in Future Information Technology (FutureTech), 2010, DOI: 10.1109/FUTURETECH.2010.5482729.
  9. Kwapisz, J. R., Weiss, G. M. and Moore, S. A., 2011, 'Activity recognition using cell phone accelerometers', ACM SIGKDD Explorations Newsletter, 31 March 2011, pp. 74-82, DOI: 10.1145/1964897.1964918.
  10. Lee, Y. S. and Cho, S. B., 2011, 'Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer', Hybrid Artificial Intelligent Systems, 2011, pp. 460-467, DOI: 10.1007/978-3-642-21219-2_58.
  11. Leutheuser, H., Schuldhaus, D. and Eskofier, B. M., 2013, 'Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-theArt Algorithms Using a Benchmark Dataset', PLoS ONE, vol 8, no. 10, DOI: 10.1371/journal.pone.0075196.
  12. Saputri, T. R. D., Khan, A. M. and Lee, S. W., 2014, 'User-Independent Activity Recognition via ThreeStage GA-Based Feature Selection', International Journal of Distributed Sensor Networks, 2014, p. 15, DOI: 10.1155/2014/706287.
  13. Shoaib, M., Bosch, S., Incel, O. D. and Scholten, H., 2015, 'A Survey of Online Activity Recognition Using Mobile Phones', Sensors, vol 15, pp. 2059-2085.
  14. Siirtola, P. and Röning, J., 2012, 'Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data', International Journal of Interactive Multimedia and Artificial Intelligence, 2012, pp. 38-45, DOI: 10.1155/2014/706287.
  15. Siirtola, P. and Roning, J., 2013, 'Ready-to-use activity recognition for smartphones', IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 16-19 April 2013, pp. 59-64, DOI: 10.1109/CIDM.2013.6597218.
  16. Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E. G. and Tsiknakis, M., 2014, 'The MobiFall Dataset: Fall Detection and Classification with a Smartphone', International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2014, p. 13, DOI: 10.4018/ijmstr.2014010103.
  17. Vavoulas, G., Pediaditis, M., Spanakis, E. and Tsiknakis, M., 2013, 'The MobiFall Dataset: An Initial Evaluation of Fall Detection Algorithms Using Smartphones', IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE).
  18. Zheng, L., Cai, Y., Lin, Z., Tang, W., Zheng, H., Shi, H., Liao, B., and Wang, J., 2014, 'A Novel Activity Recognition Approach Based on Mobile Phone', Multimedia and Ubiquitous Engineering, 2014, pp. 59-65, DOI:10.1007/978-3-642-54900-7_9.
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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

@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 - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)},
year={2016},
pages={143-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005792401430151},
isbn={978-989-758-180-9},
}


in EndNote Style

TY - CONF
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