Identifying important action primitives for high level activity recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6446 LNCS, Issue , 2010, Pages 149-162

  Manzoor, Atif ^a   Villalonga, Claudia ^b   Calatroni, Alberto ^b   Truong, Hong Linh ^a   Roggen, Daniel ^b   Dustdar, Schahram ^a   Tröster, Gerhard ^b  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b ETH ZURICH   (Switzerland)

Author keywords

action primitives; Activity recognition; smart homes

Indexed keywords

ACTION PRIMITIVES; ACTIVITY RECOGNITION; CLASSIFICATION ALGORITHM; HUMAN ACTIVITIES; HUMAN BODIES; LEVELS OF ABSTRACTION; OBJECT SENSORS; REASONING ALGORITHMS; SENSOR DATA; SENSOR SYSTEMS; SMART HOMES; STATE OF THE ART; TRANSMIT DATA; USER CENTERED DESIGNS; VISION BASED; WEARABLE SENSORS;

ALGORITHMS; EMBEDDED SYSTEMS; HUMAN COMPUTER INTERACTION; AUTOMATION; DATA MINING; INTELLIGENT BUILDINGS; PATTERN RECOGNITION; WEARABLE TECHNOLOGY;

SENSORS; WEARABLE SENSORS;

EID: 78650469386     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16982-3_12     Document Type: Conference Paper

References (15)

1. Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37-66 (1991)
2. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Proceedings of Internation Conference of Pervasive Computing 2004, pp. 1-17 (2004)
3. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10-18 (2009)
4. Intille, S., Larson, K., Tapia, E., Beaudin, J., Kaushik, P., Nawyn, J., Rockinson, R.: Using a live-in laboratory for ubiquitous computing research. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 349-365. Springer, Heidelberg (2006)
5. Lepri, B., Mana, N., Cappelletti, A., Pianesi, F., Zancanaro, M.: What is happening now? detection of activities of daily living from simple visual features. In: Personal and Ubiquitous Computing, pp. 1-18 (2010)
6. Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.: A long-term evaluation of sensing modalities for activity recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483-500. Springer, Heidelberg (2007)
7. Maekawa, T., Yanagisawa, Y., Kishino, Y., Ishiguro, K., Kamei, K., Sakurai, Y., Okadome, T.: Object-based activity recognition with heterogeneous sensors on wrist. In: Floreén, P., Krüger, A., Spasojevic, M. (eds.) Pervasive Computing. LNCS, vol. 6030, pp. 246-264. Springer, Heidelberg (2010)
8. McKeever, S., Ye, J., Coyle, L., Dobson, S.: Using dempster-shafer theory of evidence for situation inference. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds.) EuroSSC 2009. LNCS, vol. 5741, pp. 149-162. Springer, Heidelberg (2009)
9. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
10. Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. American Association for Artificial Intelligence (2005)
11. Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Creatura, M., del Millàn, R.: Collecting complex activity data sets in highly rich networked sensor environments. In: Proceedings of the 7th International Conference on Networked Sensing Systems, INSS 2010. IEEE, Los Alamitos (2010)
12. 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)
13. van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 1-9. ACM, New York (2008)
14. Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G.: Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 17-33. Springer, Heidelberg (2008)
15. Zhang, H., Jiang, L., Su, J.: Hidden naive bayes. In: Twentieth National Conference on Artificial Intelligence, pp. 919-924. AAAI Press, Menlo Park (2005)