Recognizing Activities in Multiple Contexts using Transfer Learning

Activities of daily living are good indicators of the health status of elderly. Therefore, automating the monitoring of these activities is a crucial step in future care giving. However, many models for activity recognition rely on labeled examples of activities for learning the model parameters. Due to the high variability of different contexts, parameters learned for one context can not automatically be used in another. In this paper, we present a method that allows us to transfer knowledge of activity recognition from one context to the next, a task called transfer learning. We show the effectiveness of our method using real world datasets.

[1]  S. Katz,et al.  Progress in development of the index of ADL. , 1970, The Gerontologist.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[4]  Albrecht Schmidt,et al.  Ubiquitous computing - computing in context , 2003 .

[5]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[6]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Chris Atkeson,et al.  A context-aware recognition survey for data collection using ubiquitous sensors in the home , 2005, CHI Extended Abstracts.

[8]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[9]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[10]  Rajat Raina,et al.  Constructing informative priors using transfer learning , 2006, ICML.

[11]  James Fogarty,et al.  Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition , 2006, UIST.

[12]  Jeff A. Bilmes,et al.  What HMMs Can Do , 2006, IEICE Trans. Inf. Syst..

[13]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[14]  Raymond J. Mooney,et al.  Mapping and Revising Markov Logic Networks for Transfer Learning , 2007, AAAI.

[15]  B. Kröse,et al.  Bayesian Activity Recognition in Residence for Elders , 2007 .

[16]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[17]  Luc De Raedt,et al.  Relational transformation-based tagging for human activity recognition , 2007 .

[18]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[19]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.