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Human Daily Activity and Fall Recognition Using a Smartphone’s Acceleration Sensor

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Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 736))

Abstract

As one of the fastest spreading technologies and due to their rich sensing features, smartphones have become popular elements of modern human activity recognition systems. Besides activity recognition, smartphones have also been employed with success in fall detection/recognition systems, although a combined approach has not been evaluated yet. This article presents the results of a comprehensive evaluation of using a smartphone’s acceleration sensor for human activity and fall recognition, including 12 different types of activities of daily living (ADLs) and 4 different types of falls, recorded from 66 subjects in the context of creating “MobiAct”, a publicly available dataset for benchmarking and developing human activity and fall recognition systems. An optimized feature selection and classification scheme is proposed for each, a basic, i.e. recognition of 6 common ADLs only (99.9% accuracy), and a more complex human activity recognition task that includes all 12 ADLs and 4 falls (96.8% accuracy).

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References

  1. Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: 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, Rome, Italy (2016)

    Google Scholar 

  2. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newslett. 2(2), 74–82 (2011)

    Google Scholar 

  3. Siirtola, P., Röning, J.: Recognizing human activities user-independently on smartphones based on accelerometer data. Int. J. Interact. Multimedia Artif. Intell. 1(5), 38–45 (2012)

    Article  Google Scholar 

  4. Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: 5th International Conference in Future Information Technology (FutureTech) (2010)

    Google Scholar 

  5. Lee, Y.S., Cho, S.B.: Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer. In: Hybrid Artificial Intelligent Systems, pp. 460–467 (2011)

    Google Scholar 

  6. Huq, G.B., Basilakis, J., Maeder, A.: Evaluation of tri-axial accelerometery data of falls for elderly through smart phone. In: ACSW 2016 Proceedings of the Australasian Computer Science Week Multiconference, Canberra, Australia (2016)

    Google Scholar 

  7. Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E.G., Tsiknakis, M.: The MobiFall dataset: fall detection and classification with a smartphone. Int. J. Monit. Surveill. Technol. Res. (IJMSTR) 2(1), 13 (2014)

    Google Scholar 

  8. Chatzaki, C., Pediaditis, M., Vavoulas, G., Tsiknakis, M.: Estimating normal and abnormal activities using smartphones. In: 13th International Conference on Wearable, Micro and Nano Technologies for Personalised Health, Crete, Greece (2016)

    Google Scholar 

  9. Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. In: 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2014), vol. 34, pp. 450–457 (2014)

    Google Scholar 

  10. Anjum, A., Ilyas, M.U.: Activity recognition using smartphone sensors. In: Consumer Communications and Networking Conference (CCNC), pp. 914–919, 11–14 January 2013

    Google Scholar 

  11. Buber, E., Guvensan, A.M.: Discriminative time-domain features for activity recognition on a mobile phone. In: IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6, 21–24 April 2014

    Google Scholar 

  12. Fan, L., Wang, Z., Wang, H.: Human activity recognition model based on decision tree. In: Proceedings of the 2013 International Conference on Advanced Cloud and Big Data (CBD 2013), pp. 64–68 (2013)

    Google Scholar 

  13. Siirtola, P., Roning, J.: Ready-to-use activity recognition for smartphones. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 59–64, 16–19 April 2013

    Google Scholar 

  14. Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: 8th International Conference on Intelligent Environments (IE), pp. 214–221, 26–29 June 2012

    Google Scholar 

  15. Saputri, T.R.D., Khan, A.M., Lee, S.W.: User-independent activity recognition via three-stage GA-based feature selection. Int. J. Distrib. Sens. Netw. 10(3), 15 (2014)

    Google Scholar 

  16. Hung, W., Shen, F., Wu, Y.L., Hor, M.K., Tang, C.Y.: Activity recognition with sensors on mobile devices. In: Proceedings of the 2014 International Conference on Machine Learning and Cybernetics, Lanzhou (2014)

    Google Scholar 

  17. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35395-6_30

    Chapter  Google Scholar 

  18. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.-L.: A public domain dataset for human activity recognition using smartphones. In: ESANN 2013 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence, Bruges, Belgium (2013)

    Google Scholar 

  19. Wang, A., Chuzhou, G., Yang, J., Zhao, S., Chang, C.-Y.: A comparative study on human activity recognition using inertial sensors in a smartphone. IEEE Sens. J. 16(11), 4566–4578 (2016)

    Article  Google Scholar 

  20. Zheng, L., Cai, Y., Lin, Z., Tang, W., Zheng, H., Shi, H., Liao, B., Wang, J.: A novel activity recognition approach based on mobile phone. In: Multimedia and Ubiquitous Engineering, pp. 59–65 (2014)

    Google Scholar 

  21. Albert, M.V., Kording, K., Herrmann, M., Jayaraman, A.: Fall classification by machine learning using mobile phones. PLoS ONE 7(5), e36556 (2012)

    Google Scholar 

  22. Zhao, Z., Chen, Y., Wang, S., Chen, Z.: FallAlarm: smart phone based fall detecting and positioning system. Procedia Comput. Sci. 10, 617–624 (2012)

    Article  Google Scholar 

  23. Kansiz, A.O., Guvensan, M.A., Turkmen, H.I.: Selection of time-domain features for fall detection based on supervised learning. Lecture Notes in Engineering and Computer Science, vol. 2208, no. 1, pp. 796–801 (2013)

    Google Scholar 

  24. Figueiredo, I.N., Leal, C., Pinto, L., Bolito, J., Lemos, A.: Exploring smartphone sensors for fall detection. J. Mob. User Experience 5(2), 1–17 (2016)

    Google Scholar 

  25. Vavoulas, G., Pediaditis, M., Spanakis, E., Tsiknakis, M.: The MobiFall dataset: an initial evaluation of fall detection algorithms using smartphones. In: IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE) (2013)

    Google Scholar 

  26. Reyes-Ortiz, J.-L., Smartphone-Based Human Activity Recognition. Springer, Switzerland (2015)

    Google Scholar 

  27. Leutheuser, H., Schuldhaus, D., Eskofier, B.M.: Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 8(10) (2013)

    Google Scholar 

  28. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H.: A survey of online activity recognition using mobile phones. Sensors 15, 2059–2085 (2015)

    Article  Google Scholar 

  29. Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)

    Article  Google Scholar 

  30. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

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Acknowledgements

The authors gratefully thank all volunteers for their contribution in the production of the MobiAct dataset.

Author information

Authors and Affiliations

  1. Department of Informatics Engineering, Biomedical Informatics and eHealth Laboratory, Technological Educational Institute of Crete, Estavromenos, 71004, Heraklion, Crete, Greece

    Charikleia Chatzaki, Matthew Pediaditis, George Vavoulas & Manolis Tsiknakis

  2. Computational BioMedicine Laboratory, Foundation for Research and Technology – Hellas, Institute of Computer Science, Vassilika Vouton, 71110, Heraklion, Crete, Greece

    Charikleia Chatzaki, Matthew Pediaditis & Manolis Tsiknakis

Authors
  1. Charikleia Chatzaki
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  2. Matthew Pediaditis
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  3. George Vavoulas
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  4. Manolis Tsiknakis
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Corresponding author

Correspondence to Charikleia Chatzaki .

Editor information

Editors and Affiliations

  1. Fraunhofer Institute of Optronics, System Technology and Image Exploitation, Lemgo, Germany

    Carsten Röcker

  2. Imperial College London, London, United Kingdom

    John O'Donoghue

  3. RWTH Aachen University, Aachen, Germany

    Martina Ziefle

  4. School of Computing, Dublin City University, Dublin 9, Ireland

    Markus Helfert

  5. Centre for Gerontology, Dublin City University, Cork, Ireland

    William Molloy

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Chatzaki, C., Pediaditis, M., Vavoulas, G., Tsiknakis, M. (2017). Human Daily Activity and Fall Recognition Using a Smartphone’s Acceleration Sensor. In: Röcker, C., O'Donoghue, J., Ziefle, M., Helfert, M., Molloy, W. (eds) Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE 2016. Communications in Computer and Information Science, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-62704-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-62704-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62703-8

  • Online ISBN: 978-3-319-62704-5

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