![](bgb.png)
Berchtold, M., Budde, M., Gordon, D., Schmidtke, H. R.,
and Beigl, M. (2010). Actiserv: Activity recognition
service for mobile phones. In International Symposium
on Wearable Computers (ISWC), pages 1–8. IEEE.
Bouten, C. V., Koekkoek, K. T., Verduin, M., Kodde, R., and
Janssen, J. D. (1997). A triaxial accelerometer and
portable data processing unit for the assessment of
daily physical activity. IEEE Transactions on Biomed-
ical Engineering, 44(3):136–147.
Chang, K.-H., Chen, M. Y., and Canny, J. (2007). Tracking
free-weight exercises. In International Conference on
Ubiquitous Computing, pages 19–37. Springer.
Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y.,
Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A.,
LeGrand, L., Libby, R., et al. (2008). Activity sensing
in the wild: a field trial of ubifit garden. In Proceed-
ings of the SIGCHI Conference on Human Factors in
Computing Systems, pages 1797–1806. ACM.
Dashdorj, Z. and Sobolevsky, S. (2015). Characterization of
behavioral patterns exploiting description of geograph-
ical areas. CoRR, abs/1510.02995.
Eagle, N. and Pentland, A. S. (2006). Reality mining: sens-
ing complex social systems. Personal and ubiquitous
computing, 10(4):255–268.
Farringdon, J., Moore, A. J., Tilbury, N., Church, J., and
Biemond, P. D. (1999). Wearable sensor badge and
sensor jacket for context awareness. In Wearable Com-
puters. Digest of Papers. The Third International Sym-
posium on, pages 107–113. IEEE.
Ghosh, S. and Ghosh, S. K. (2016). Thump: Semantic anal-
ysis on trajectory traces to explore human movement
pattern. In Proceedings of the 25th International Con-
ference Companion on World Wide Web, pages 35–36.
Jensen, C. S., Lahrmann, H., Pakalnis, S., and Runge, J.
(2004). The infati data. arXiv preprint cs/0410001.
Junker, H., Lukowicz, P., and Troster, G. (2004). Sampling
frequency, signal resolution and the accuracy of wear-
able context recognition systems. In Wearable Com-
puters, Eighth International Symposium on, volume 1,
pages 176–177. IEEE.
Kern, N., Schiele, B., and Schmidt, A. (2003). Multi-sensor
activity context detection for wearable computing. In
European Symposium on Ambient Intelligence, pages
220–232. Springer.
Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., and
Laurila, J. (2010). Towards rich mobile phone datasets:
Lausanne data collection campaign. Proc. ICPS,
Berlin.
Laurila, J. K., Gatica-Perez, D., Aad, I., Blom, J., Bornet,
O., Do, T. M. T., Dousse, O., Eberle, J., Miettinen, M.,
Liao, L., Fox, D., and Kautz, H. (2013). From big
smartphone data to worldwide research: The Mobile
Data Challenge. The International Journal of Robotics
Research, 26(6):119–134.
Lester, J., Choudhury, T., Kern, N., Borriello, G., and Han-
naford, B. (2005). A hybrid discriminative/generative
approach for modeling human activities. In Proceed-
ings of the 19th International Joint Conference on Ar-
tificial Intelligence, pages 766–772.
Lung, H.-Y., Chung, C.-H., and Dai, B.-R. (2014). Predict-
ing locations of mobile users based on behavior seman-
tic mining. In Pacific-Asia Conference on Knowledge
Discovery and Data Mining, pages 168–180. Springer.
Mantyjarvi, J., Himberg, J., and Seppanen, T. (2001). Recog-
nizing human motion with multiple acceleration sen-
sors. In Systems, Man, and Cybernetics, IEEE Interna-
tional Conference on, volume 2, pages 747–752. IEEE.
Montoliu, R., Mart
´
ınez-Uso, A., Mart
´
ınez-Sotoca, J., and
McInerney, J. (2012). Semantic place prediction by
combining smart binary classifiers. In Nokia Mobile
Data Challenge Workshop., volume 1.
Perrin, O., Terrier, P., Ladetto, Q., Merminod, B., and Schutz,
Y. (2000). Improvement of walking speed prediction
by accelerometry and altimetry, validated by satellite
positioning. Medical and Biological Engineering and
Computing, 38(2):164–168.
Preece, S. J., Goulermas, J. Y., Kenney, L. P., Howard, D.,
Meijer, K., and Crompton, R. (2009). Activity identifi-
cation using body-mounted sensorsa review of clas-
sification techniques. Physiological measurement,
30(4):R1.
Ravi, N., Dandekar, N., Mysore, P., and Littman, M. L.
(2005). Activity recognition from accelerometer data.
In AAAI, volume 5, pages 1541–1546.
Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., and
Srivastava, M. (2010). Using mobile phones to de-
termine transportation modes. ACM Transactions on
Sensor Networks (TOSN), 6(2):13.
Siewiorek, D. P., Smailagic, A., Furukawa, J., Krause, A.,
Moraveji, N., Reiger, K., Shaffer, J., and Wong, F. L.
(2003). Sensay: A context-aware mobile phone. In
ISWC, volume 3, page 248.
Stikic, M., Van Laerhoven, K., and Schiele, B. (2008). Ex-
ploring semi-supervised and active learning for activ-
ity recognition. In 12th International Symposium on
Wearable Computers, pages 81–88. IEEE.
Yu Zheng, Hao Fu, X. X. W.-Y. M. Q. L. (2011). Geolife
GPS trajectory dataset - User Guide.
Zhu, Y., Zhong, E., Lu, Z., and Yang, Q. (2012). Feature
engineering for place category classification. Mobile
Data Challenge 2012.
Zinnen, A., Blanke, U., and Schiele, B. (2009). An analy-
sis of sensor-oriented vs. model-based activity recog-
nition. In International Symposium on Wearable Com-
puters, pages 93–100. IEEE.
Sensor Fusion for Semantic Place Labeling
131