Learning and Recognizing the Places We Go
Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someone’s personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.
KeywordsGlobal Position System Mobile Device Data Collector Ubiquitous Computing Visit Frequency
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- 2.LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.: Place lab: Device positioning using radio beacons in the wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005) (to appear)CrossRefGoogle Scholar
- 5.Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting places from traces of locations. In: Proceedings of the Second ACM International Workshop onWireless Mobile Applications and Services onWLAN Hotspots (WMASH 2004), Philadelphia, PA, pp. 110–118. ACM Press, New York (2004)CrossRefGoogle Scholar
- 6.Laasonen, K., Raento, M., Toivonen, H.: Adaptive on-device location recognition. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 287–304. Springer, Heidelberg (2004)Google Scholar
- 7.Trevisani, E., Vitaletti, A.: Cell-id location technique, limits and benefits: An experimental study. In: Proceedings of the 6th IEEEWorkshop on Mobile Computing Systems & Applications (WMCSA 2004), IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
- 8.Clarkson, B.P., Pentland, A.: Unsupervised clustering of ambulatory audio and video. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 6, pp. 3037–3040. Springer, Heidelberg (1999)Google Scholar
- 10.Patterson, D.J., Liao, L., Gajos, K., Collier, M., Livic, N., Olson, K., Wang, S., Fox, D., Kautz, H.: Opportunity knocks: a system to provide cognitive assistance with transportation services. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 433–450. Springer, Heidelberg (2004)CrossRefGoogle Scholar