Abstract
The ubiquity of smartphones has motivated efforts to use the embedded sensors to detect various aspects of user context to transparently provide personalized and contextualized services to the user. One relevant piece of context is the physical activity of the smartphone user. In this paper, we propose a novel set of features for distinguishing five physical activities using only sensors embedded in the smartphone. Specifically, we introduce features that are normalized using the orientation sensor such that horizontal and vertical movements are explicitly computed. We evaluate a neural network classifier in experiments in the wild with multiple users and hardware, we achieve accuracies above 90% for a single user and phone, and above 65% for multiple users, which is higher that similar works on the same set of activities, demonstrating the potential of our approach.
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References
Bonomi, A., Goris, A., Yin, B., Westerkerp, K.: Detection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer. Journal of Medicine & Science in Sports & Exercise 41(9), 1770–1777 (2009)
Boyle, M., Klausner, A., Starobinski, D., Trachtenberg, A., Wu, H.: Poster: gait-based smartphone user identification. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys 2011, p. 395. ACM Press (June 2011)
Hynes, M., Wang, H., McCarrick, E., Kilmartin, L.: Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets. Personal and Ubiquitous Computing 15(7), 667–678 (2010)
Martin, J.J.: Recognition of motion patterns based on mobile sensor data. Msc, University of Stuttgart (2010)
Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine 10(1), 156–167 (2006)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74 (2011)
Lee, R.Y.W., Carlisle, A.J.: Detection of falls using accelerometers and mobile phone technology. Age and Ageing 40(6), 690–696 (2011)
Longstaff, B., Reddy, S., Estrin, D.: Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: Proceedings of the 4th International ICST Conference on Pervasive Computing Technologies for Healthcare, pp. 1–7. IEEE (2010)
Rabin, C., Bock, B.: Desired features of smartphone applications promoting physical activity. Telemedicine Journal and e-health: The Official Journal of the American Telemedicine Association 17(10), 801–803 (2011)
Weiss, G.M., Lockhart, J.W.: Identifying user traits by mining smart phone accelerometer data. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, SensorKDD 2011, pp. 61–69. ACM Press (August 2011)
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Prudêncio, J., Aguiar, A., Lucani, D. (2013). Physical Activity Recognition from Smartphone Embedded Sensors. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_102
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DOI: https://doi.org/10.1007/978-3-642-38628-2_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
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