Action2Activity: Recognizing complex activities from sensor data

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2015-January, Issue , 2015, Pages 1617-1623

  Liu, Ye ^a   Nie, Liqiang ^a   Han, Lei ^b   Zhang, Luming ^a   Rosenblum, David S ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECOGNITION; MOTION ESTIMATION;

ACTION RECOGNITION; COMPLEX ACTIVITY; INTRINSIC PROPERTY; MID-LEVEL FEATURES; MULTITASK LEARNING; SENSOR DATA; TEMPORAL PATTERN MININGS; TWO-COMPONENT;

ARTIFICIAL INTELLIGENCE;

EID: 84949742707     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the International Conference on Very Large Data Bases, 1994.
2. James F Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, 1983.
3. Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. Society for Industrial and Applied Mathematics Journal on Imaging Sciences, 2009.
4. Ricardo Chavarriaga, Hesam Sagha, Alberto Calatroni, Sundara Tejaswi Digumarti, Gerhard Tröster, José del R Millán, and Daniel Roggen. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 2013.
5. Tao Gu, Zhanqing Wu, Xianping Tao, Hung Keng Pung, and Jian Lu. epsicar: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In Proceedings of IEEE International Conference on Pervasive Computing and Communications, 2009.
6. Frank Höppner. Discovery of temporal patterns. In Principles of Data Mining and Knowledge Discovery. 2001.
7. Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, and Kiyoshi Asai. Multi-task learning via conic programming. In Advances in Neural Information Processing Systems, 2008.
8. John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. 2001.
9. Liqiang Nie, Yi-Liang Zhao, M. Akbari, Jialie Shen, and Tat-Seng Chua. Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Transactions on Knowledge and Data Engineering, 2015.
10. Julien Pansiot, Danail Stoyanov, Douglas McIlwraith, Benny P.L. Lo, and G.Z. Yang. Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems. In Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks. 2007.
11. Dhaval Patel, Wynne Hsu, and Mong Li Lee. Mining relationships among interval-based events for classification. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 2008.
12. Lawrence R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989.
13. Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L. Littman. Activity recognition from accelerometer data. In Proceedings of the Conference on Innovative Applications of Artificial Intelligence, 2005.
14. Michael S Ryoo and Jake K Aggarwal. Recognition of composite human activities through context-free grammar based representation. In Proceedings of the Conference on Computer Vision and Pattern Recognition, 2006.
15. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 1996.
16. Xinxi Wang, David Rosenblum, and Ye Wang. Context-aware mobile music recommendation for daily activities. In Proceedings of the ACM International Conference on Multimedia, 2012.
17. Shin-Yi Wu and Yen-Liang Chen. Mining nonambiguous temporal patterns for intervalbased events. IEEE Transactions on Knowledge and Data Engineering, 2007.
18. Qiang Yang. Activity recognition: Linking low-level sensors to high-level intelligence. In Proceedings of the International Joint Conference on Artificial Intelligence, 2009.
19. Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B, 2006.
20. Yu Zhang and Dit-Yan Yeung. A convex formulation for learning task relationships in multi-task learning. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2010.
21. Yu Zhang and Dit-Yan Yeung. Learning high-order task relationships in multi-task learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 2013.
22. Yongmian Zhang, Yifan Zhang, Eran Swears, Natalia Larios, Ziheng Wang, and Qiang Ji. Modeling temporal interactions with interval temporal bayesian networks for complex activity recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013.
23. Jiayu Zhou, Jianhui Chen, and Jieping Ye. Clustered multi-task learning via alternating structure optimization. In Advances in Neural Information Processing Systems, 2011.
24. Jiayu Zhou, Lei Yuan, Jun Liu, and Jieping Ye. A multi-task learning formulation for predicting disease progression. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
25. Hankz Hankui Zhuo, Derek Hao Hu, Chad Hogg, Qiang Yang, and Hector Munoz-Avila. Learning htn method preconditions and action models from partial observations. In Proceedings of the International Joint Conference on Artificial Intelligence, 2009.