Abstract
This paper describes the use of a prompted labeling solution to obtain class labels for user activity and context information on a mobile device. Based on the output from an activity recognition module, the prompt labeling module polls for class transitions from any of the activities (e.g. walking, running) to the standing still activity. Once a transition has been detected the system prompts the user, through the provision of a message on the mobile phone, to provide a label for the last activity that was carried out. This label, along with the raw sensor data is then stored locally prior to being uploaded to cloud storage. The paper provides technical details of how and when the system prompts the user for an activity label and discusses the information that can be gleaned from sensor data. This system allows for activity and context information to be collected on a large scale. Data can then be used within new opportunities in data mining and modeling of user context for a variety of applications.
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References
Intille, S.S., Lester, J., Sallis, J.F., et al.: New Horizons in Sensor Development. Medicine & Science in Sports & Exercise 44, S24–S31 (2012)
Hamm, J., Stone, B., Belkin, M., Dennis, S.: Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams. In: Uhler, D., Mehta, K., Wong, J.L. (eds.) MobiCASE 2012. LNICST, vol. 110, pp. 328–342. Springer, Heidelberg (2013)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., et al.: Activity Identification using Body-Mounted sensors—a Review of Classification Techniques. Physiol. Meas. 30, R1–R33 (2009)
Avci, A., Bosch, S., Marin-Perianu, M., et al.: Activity Recognition using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey, pp. 1–10 (2010)
Hossmann, T., Efstratiou, C., Mascolo, C.: Collecting Big Datasets of Human Activity One Checkin at a Time, pp. 15–20 (2012)
Lane, N.D., Miluzzo, E., Lu, H., et al.: A Survey of Mobile Phone Sensing. IEEE Communications Magazine 48, 140–150 (2010)
Krishnan, N.C., Colbry, D., Juillard, C., et al.: Real Time Human Activity Recognition using Tri-Axial Accelerometers, pp. 1–5 (2008)
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)
Parkka, J., Ermes, M., Korpipaa, P., et al.: Activity Classification using Realistic Data from Wearable Sensors. IEEE Transactions on Information Technology in Biomedicine 10, 119–128 (2006)
Mannini, A., Intille, S.S., Rosenberger, M., et al.: Activity Recognition using a Single Accelerometer Placed at the Wrist Or Ankle. Med. Sci. Sports Exerc. (2013); E-Published ahead of Print
Plotz, T., Chen, C., Hammerla, N.Y., et al.: Automatic Synchronization of Wearable Sensors and Video-Cameras for Ground Truth Annotation–A Practical Approach, pp. 100–103 (2012)
Cruciani, F., Donnelly, M.P., Nugent, C.D., Parente, G., Paggetti, C., Burns, W.: DANTE: A video based annotation tool for smart environments. In: Par, G., Morrow, P. (eds.) S-CUBE 2010. LNICST, vol. 57, pp. 179–188. Springer, Heidelberg (2011)
Lasecki, W.S., Song, Y.C., Kautz, H., et al.: Real-Time Crowd Labeling for Deployable Activity Recognition, pp. 1203–1212 (2013)
Kawaguchi, N., Watanabe, H., Yang, T., et al.: HASC2012corpus: Large Scale Human Activity Corpus and its Application (2012)
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
Harada, S., Lester, J., Patel, K., et al.: VoiceLabel: Using Speech to Label Mobile Sensor Data, pp. 69–76 (2008)
Han, M., Lee, Y., Lee, S.: Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone. Sensors 12, 12588–12605 (2012)
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Cleland, I. et al. (2013). Mobile Based Prompted Labeling of Large Scale Activity Data. In: Nugent, C., Coronato, A., Bravo, J. (eds) Ambient Assisted Living and Active Aging. IWAAL 2013. Lecture Notes in Computer Science, vol 8277. Springer, Cham. https://doi.org/10.1007/978-3-319-03092-0_2
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DOI: https://doi.org/10.1007/978-3-319-03092-0_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03091-3
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