Abstract
Activity Recognition (AR) from smartphone sensors has become a hot topic in the mobile computing domain since it can provide services directly to the user (health monitoring, fitness, context-awareness) as well as for third party applications and social network (performance sharing, profiling). Most of the research effort has been focused on direct recognition from accelerometer sensors and few studies have integrated the audio channel in their model despite the fact that it is a sensor that is always available on all kinds of smartphones. In this study, we show that audio features bring an important performance improvement over an accelerometer based approach. Moreover, the study demonstrates the interest of considering the smartphone location for on-line context-aware AR and the prediction power of audio features for this task. Finally, another contribution of the study is the collected corpus that is made available to the community for AR recognition from audio and accelerometer sensors.
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References
Ainsworth, B.E., Haskell, W.L., Herrmann, S.D., Meckes, N., Bassett, D.R., Tudor-Locke, C., Greer, J.L., Vezina, J., Whitt-Glover, M.C., Leon, A.S.: 2011 compendium of physical activities: a second update of codes and met values. Medicine and Science in Sports and Exercise 43(8), 1575–1581 (2011)
Alanezi, K., Mishra, S.: Impact of smartphone position on sensor values and context discovery. http://digitool.library.colostate.edu/exlibris/dtl/d3_1/apache_media/L2V4bGlicmlzL2R0bC9kM18xL2FwYWNoZV9tZWRpYS8yMTIyNjM=.pdf
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)
Blachon, D., Portet, F., Besacier, L., Tassart, S.: RecordMe: A Smartphone Application for Experimental Collections of Large Amount of Data Respecting Volunteer’s Privacy. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds.) UCAmI 2014. LNCS, vol. 8867, pp. 345–348. Springer, Heidelberg (2014)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chon, Y., Talipov, E., Cha, H.: Autonomous management of everyday places for a personalized location provider. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(4), 518–531 (2012)
Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is key. Communications of the ACM 48(3), 49–53 (2005)
Cvetković, B., Kaluža, B., Milić, R., Luštrek, M.: Towards human energy expenditure estimation using smart phone inertial sensors. In: Augusto, J.C., Wichert, R., Collier, R., Keyson, D., Salah, A.A., Tan, A.-H. (eds.) AmI 2013. LNCS, vol. 8309, pp. 94–108. Springer, Heidelberg (2013)
Diaconita, I., Reinhardt, A., Englert, F., Christin, D., Steinmetz, R.: Do you hear what i hear? using acoustic probing to detect smartphone locations. In: 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 1–9. IEEE (2014)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10(4), 255–268 (2006)
Incel, O.D., Kose, M., Ersoy, C.: A review and taxonomy of activity recognition on mobile phones. BioNanoScience 3(2), 145–171 (2013)
Kose, M., Incel, O.D., Ersoy, C.: Online human activity recognition on smart phones. In: Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, pp. 11–15 (2012)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289 (2001)
Miluzzo, E., Papandrea, M., Lane, N.D., Lu, H., Campbell, A.T.: Pocket, bag, hand, etc.-automatically detecting phone context through discovery. In: Proc. PhoneSense 2010, pp. 21–25 (2010)
Park, J.-G., Patel, A., Curtis, D., Teller, S., Ledlie, J.: Online pose classification and walking speed estimation using handheld devices. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 113–122. ACM (2012)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN) 6(2), 13 (2010)
Segal, M.R.: Machine learning benchmarks and random forest regression. Technical report, University of California (2004)
Sutton, C., Mccallum, A.: Introduction to Conditional Random Fields for Relational Learning. MIT Press (2006)
van Kasteren, T.L.M., Englebienne, G., Kröse, B.J.A.: An activity monitoring system for elderly care using generative and discriminative models. Personal and Ubiquitous Computing 14(6), 489–498 (2010)
Yang, J.: Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, pp. 1–10. ACM (2009)
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Blachon, D., Coşkun, D., Portet, F. (2014). On-line Context Aware Physical Activity Recognition from the Accelerometer and Audio Sensors of Smartphones. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_17
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DOI: https://doi.org/10.1007/978-3-319-14112-1_17
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