Abstract
This paper uses accelerometer-embedded mobile phones to monitor one’s daily physical activities for sake of changing people’s sedentary lifestyle. In contrast to the previous work of recognizing user’s physical activities by using a single accelerometer-embedded device and placing it in a known position or fixed orientation, this paper intends to recognize the physical activities in the natural setting where the mobile phone’s position and orientation are varying, depending on the position, material and size of the hosting pocket. By specifying 6 pocket positions, this paper develops a SVM based classifier to recognize 7 common physical activities. Based on 10-folder cross validation result on a 48.2 hour data set collected from 7 subjects, our solution outperforms Yang’s solution and SHPF solution by 5~6%. By introducing an orientation insensitive sensor reading dimension, we boost the overall F-score from 91.5% to 93.1%. With known pocket position, the overall F-score increases to 94.8%.
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References
World Health Organization: Move for Health, http://www.who.int/moveforhealth/en/
Manson, J.E., Skerrett, P.J., Greenland, P., VanItallie, T.B.: The Escalating Pandemics of Obesity and Sedentary Lifestyle: A Call to Action for Clinicians. Arch. Intern. Med. 164(3), 249–258 (2004)
Consolvo, S., et al.: Activity Sensing in the Wild: A Field Trial of UbiFit Garden. In: CHI 2008 (2008)
Lin, J., Mamykina, L., Lindtner, S., Delajoux, G., Strub, H.: Fish’n’Steps: Encouraging Activitiy with an Interactive Computer Game. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 261–278. Springer, Heidelberg (2006)
Anderson, I., Maitlan, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., Muller, H.: Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones. Mobile Networks and Applications, 185–199 (2007)
Maitland, J., Sherwood, S., Barkhuus, L., Anderson, I., Hall, M., Brown, B., Chalmers, M., Muller, H.: Increasing the Awareness of Daily Activity Levels with Pervasive Computing. In: Proc. of Pervasive Health 2006 (2006)
Pavan, T., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine Recognition of Human Activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11) (2008)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Reviews 34(3) (2004)
Fujiki, Y.: iPhone as a Physical Activity Measurement Platform. In: CHI 2010 USA (2010)
Ichikawa, F., Chipchase, J., Grignani, R.: Where is the Phone? A Study of Mobile Phone Location in Public Spaces. In: The Second International Conference on Mobile Technology, Application and Systems, pp. 797–804 (2005)
Bao, L., Intille, 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)
Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltolla, J., Korhonen, I.: Activity Classification Using Realistic Data From Wearable Sensors. IEEE Transactions on Information Technology in Biomedicine, 119–128 (2006)
Lester, J., Choudhury, T., Kern, N., Borriello, G., Hannaford, B.: A hybrid discriminative /generative approach for modeling human activities. In: Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 776–772 (2005)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: AAAI, pp. 1541–1546 (2005)
Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. In: Proc. Of the International Workshop on Wearable and Implantable Body Sensor Netowrks (BSN 2006), pp. 113–116 (2006)
Lester, J., Choudhury, T., Kern, N., Borriello, G.: A Practical Approach to Recognize Physical Activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)
Yang, J.: Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones. In: IMCE 2009 Beijing, China (2009)
Mizell, D.: Using gravity to estimate accelerometer orientation. In: ISWC 2003, Proc. Of the 7th IEEE International Symposium on Wearable Computers, USA, p. 252 (2003)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture Recognition with a 3-D Accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001) Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Baek, J., Kim, S., Kim, H., Cho, J., Yun, B.: Recognition of User Activity for User Interface on a Mobile Device. In: Proc. of the 24th South East Asia Regional Computer Conference, Thailand (2007)
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Sun, L., Zhang, D., Li, B., Guo, B., Li, S. (2010). Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds) Ubiquitous Intelligence and Computing. UIC 2010. Lecture Notes in Computer Science, vol 6406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16355-5_42
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DOI: https://doi.org/10.1007/978-3-642-16355-5_42
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