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Activity Classification Using a Single Tri-axial Accelerometer of Smartphone

  • Seonguk Heo
  • Kyuchang Kang
  • Changseok Bae
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 182)

Abstract

Activity recognition and classification is very useful filed. In this paper, we present the activity classification using a single tri-axial accelerometer of smartphone. Smartphones have a lot of sensors and a powerful performance, and many researchers study using smartphone sensors. Especially, utilization with the accelerometer is very large. The topic of the activity classification can be used as many parts, such as health-care part, medical part, and emergency part. We want to make the activity predictive model in everyday life using the activity classification. To make this model, user’s activity should be classified and recorded. In order to classify the daily activity, some elements should be considered. In this paper, we analyze to find the optimized environments for activity classification.

Keywords

Activity classification Accelerometer Smartphone Daily activity 

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References

  1. 1.
    Yang, J.-Y., Chen, Y.-P., Lee, G.-Y., Liou, S.-N., Wang, J.-S.: Activity Recognition Using One Triaxial Accelerometer: A Neuro-fuzzy Classifier with Feature Reduction. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds.) ICEC 2007. LNCS, vol. 4740, pp. 395–400. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I.: Activity Classification Using Realistic Data From Wearable Sensors. IEEE Transactions on Information Technology in Biomedicine, 119–128 (2006)Google Scholar
  3. 3.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: IAAA 2005 Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence, pp. 1541–1546 (2005)Google Scholar
  4. 4.
    Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity Recognition and Monitoring Using Multiple Sensors on Different Body Position. In: International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006), pp. 113–116 (2006)Google Scholar
  5. 5.
    Lau, S.L., David, K.: Movement Recognition using the Accelerometer in Smartphones. In: Future Network and Mobile Summit, pp. 1–9 (2010)Google Scholar
  6. 6.
    Lee, Y.-S., Cho, S.-B.: Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer. In: The 6th International Conference on Hybrid Artificial Intelligent Systems, pp. 460–467 (2011)Google Scholar
  7. 7.
    Lau, S.L., Konig, I., David, K., Parandian, B., Carius-Dussel, C., Schultz, M.: Supporting Patient Monitoring Using Activity Recognition with a Smartphone. In: Wireless Communication Systems (ISWCS), pp. 810–814 (2010)Google Scholar
  8. 8.
    Stikic, M., Huyunh, T., Van Laerhoven, K., Schiele, B.: ADL Recognition Based on the Combination of RFID and Accelerometer Sensing. In: Pervasive Computing Technologies for Healthcare, pp. 258–263 (2008)Google Scholar
  9. 9.
    Jafari, R., Li, W., Bajcsy, R., Glaser, S., Sastry, S.: Physical Activity Monitoring for Assisted Living at Home. In: 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), pp. 213–219 (2007)Google Scholar
  10. 10.
    Bieber, G., Voskamp, J., Urban, B.: Activity Recognition for Everyday Life on Mobile Phones. In: Universal Access in Human-Computer Interaction. Intelligent and Ubiquitous Interaction Environments, pp. 289–296 (2009)Google Scholar
  11. 11.
    Lee, M.-W., Khan, A.M., Kim, J.-H., Cho, Y.-S., Kim, T.-S.: A Single Tri-axial Accelerometer-based Real-time Personal Life Log System Capable of Activity Classification and Exercise Information Generation. In: Engineering in Medicine and Biology Society (EMBC), pp. 1390–1393 (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.University of Science and TechnologyDaejeonKorea
  2. 2.Electronics and Telecommunications Research InstituteDaejeonKorea

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