An integrated framework for human activity recognition

UbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Volumn , Issue , 2012, Pages 621-622

  Cao, Hong ^a   Nguyen, Minh Nhut ^a   Phua, Clifton ^a   Krishnaswamy, Shonali ^a,b   Li, Xiao Li ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b MONASH UNIVERSITY   (Australia)

Author keywords

Activity recognition; Data mining; Imbalance; Learning; Smoothing; Ubiquitous computing; Wearable computing

Indexed keywords

DATA MINING; HIDDEN MARKOV MODELS; PATTERN RECOGNITION; SEQUENTIAL MACHINES; SUPPORT VECTOR MACHINES;

ACTIVITY RECOGNITION; IMBALANCE; LEARNING; SMOOTHING; WEARABLE COMPUTING;

UBIQUITOUS COMPUTING;

EID: 84867457327     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2370216.2370334     Document Type: Conference Paper

References (6)

1. Sagha, H., Digumarti, S.T., et al.: Benchmarking Classification Techniques using the Opportunity Human Activity Dataset. In: IEEE Int. Conf. on Systems, Man, and Cybernetics (2011).
2. Albinali, F., Davies, N. and Friday, A. Structural Learning of Activities from Sparse Datasets. In: PerCom'07, 221-228, (2007).
3. Stager, M, Lukowicz, P. and Troster, G. Dealing with Class Skew in Context Recognition, Proc. ICDCSW'06, pp. 58-63, (2006).
4. Cao, H., Li, X.-L., Woon, Y.-K., Ng, S.-K.: SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification. In: Proc. IEEE Int. Conf. on Data Mining, (2011).
5. Chawla, N.V., Bowyer, K.W., et al.: SMOTE: Synthetic Minority Over-Sampling Technique. J. Artificial Intelligence, 16, 321-357, (2002).
6. Lester, J., Choudhury, T., et al.: A Hybrid Discriminative/Generative Approach for Modeling Human Activities. In: IJCAI'05, 766-772, (2005).