Zack Zhu
ABSTRACT
Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Second, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data as the basis for in-situ activity recognition. By treating the user as the "sensor", we make use of implicit signals emitted from natural use of mobile smart-phones. Applying an L1-regularized Linear SVM on features derived from textual content, semantic location, and time, we are able to infer 10 meaningful classes of daily life activities with a mean accuracy of up to 83.9%. Our work illustrates a promising first step towards comprehensive, high-level activity recognition using free, crowd-generated, social media data.
- L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Proceedings of the 2nd International Conference on Pervasive Computing, pages 1--17, April 2004.Google Scholar
Cross Ref
- U. Blanke and B. Schiele. Daily routine recognition through activity spotting. In 4rd International Symposium on Location- and Context-Awareness (LoCA), 2009. Google ScholarDigital Library
- M. Borazio and K. Van Laerhoven. Improving activity recognition without sensor data: a comparison study of time use surveys. In Proceedings of the 4th Augmented Human International Conference, pages 108--115. ACM, 2013. Google ScholarDigital Library
- D. Dearman, T. Sohn, and K. N. Truong. Opportunities exist: continuous discovery of places to perform activities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '11, pages 2429--2438, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- D. Dearman and K. N. Truong. Identifying the activities supported by locations with community-authored content. In Proceedings of the 12th ACM international conference on Ubiquitous computing, Ubicomp '10, pages 23--32, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 2008. Google ScholarDigital Library
- I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1--3): 389--422, 2002. Google ScholarDigital Library
- T. Huynh, U. Blanke, and B. Schiele. Scalable recognition of daily activities with wearable sensors. In 3rd International Workshop on Location- and Context- Awareness (LoCA 2007), page 50--67, september 2007. Google ScholarDigital Library
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65. ACM, 2007. Google ScholarDigital Library
- T. Joachims. Text categorization with suport vector machines: Learning with many relevant features. In ECML98, 1998. Google ScholarDigital Library
- S. Katz, T. Downs, H. Cash, and R. Grotz. Progress in development of the index of ADL. The Gerontologist, 10(1 Part 1): 20, 1970.Google Scholar
- N. Kawaguchi, N. Ogawa, Y. Iwasaki, K. Kaji, T. Terada, K. Murao, S. Inoue, Y. Kawahara, Y. Sumi, and N. Nishio. Hasc challenge: gathering large scale human activity corpus for the real-world activity understandings. In Proceedings of the 2nd Augmented Human International Conference, page 27. ACM, 2011. Google ScholarDigital Library
- L. Liao, D. Fox, and H. Kautz. Extracting places and activities from gps traces using hierarchical conditional random fields. Int. J. Rob. Res., 26(1): 119--134, Jan. 2007. Google ScholarDigital Library
- D. Minnen, T. Starner, I. Essa, and C. Isbell. Discovering characteristic actions from on-body sensor data. In Proceedings of the 10th IEEE International Symposium on Wearable Computers (ISWC), 2006.Google ScholarCross Ref
- T. H. Monk, E. Frank, J. M. Potts, and D. J. Kupfer. A simple way to measure daily lifestyle regularity. In European Sleep Research Society, 2002.Google ScholarCross Ref
- M. Naaman, J. Boase, and C.-H. Lai. Is it really about me?: message content in social awareness streams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189--192. ACM, 2010. Google ScholarDigital Library
- A. Oulasvirta, E. Lehtonen, E. Kurvinen, and M. Raento. Making the ordinary visible in microblogs. Personal and ubiquitous computing, 14(3): 237--249, 2010. Google ScholarDigital Library
- R. Pan, M. Ochi, and Y. Matsuo. Discovering behavior patterns from social data for managing personal life. In 2013 AAAI Spring Symposium Series, 2013.Google Scholar
- K. Partridge and P. Golle. On using existing time-use study data for ubiquitous computing applications. In Proceedings of the 10th international conference on Ubiquitous computing, pages 144--153. ACM, 2008. Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011. Google ScholarDigital Library
- M. Perkowitz, M. Philipose, K. Fishkin, and D. J. Patterson. Mining models of human activities from the web. In Proceedings of the 13th international conference on World Wide Web, WWW '04, pages 573--582, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- K. J. Shelley. Developing the american time use survey activity classification system. Monthly Lab. Rev., 128: 3, 2005.Google Scholar
- G. Tsoumakas and I. Katakis. Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM), 3(3): 1--13, 2007.Google Scholar
- Twitter. 200 million tweets per day. https://blog.twitter.com/2011/200-million-tweets-day, June 2011.Google Scholar
- Twitter. Twitter turns six. https://blog.twitter.com/2012/twitter-turns-six, Mar. 2012.Google Scholar
- K. Van Laerhoven, D. Kilian, and B. Schiele. Using rhythm awareness in long-term activity recognition. In Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on, pages 63--66. IEEE, 2008. Google ScholarDigital Library
- J. Ye, A. K. Clear, L. Coyle, and S. Dobson. On using temporal features to create more accurate human-activity classifiers. In Artificial Intelligence and Cognitive Science, pages 273--282. Springer, 2010. Google ScholarDigital Library
- J. Ye, L. Coyle, S. Dobson, and P. Nixon. Using situation lattices in sensor analysis. In Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on, pages 1--11. IEEE, 2009. Google ScholarDigital Library
- Z. Zhu, U. Blanke, A. Calatroni, and G. Tröster. Prior knowledge of human activities from social data. In Proceedings of the 17th International Symposium on Wearable Computers (ISWC '13), 2013. Google ScholarDigital Library
Index Terms
- Human activity recognition using social media data
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