ARHMAM: an activity recognition system based on hidden Markov minded activity model

The fundamental problem of the existing Activity Recognition (AR) systems is that these require real-world activity data to train the underneath activity classifier. It significantly reduces the applicability and scalability of the system. An AR system trained in an environment would only be applicable to that environment and would not be able to recognize new activities of interest. To overcome such difficulties, in this paper we propose a simple and ubiquitous sensor based AR system that uses web activity data to train its classifier. It would work to almost any environment and would be scalable by its very design. Given a set of activities to monitor, object names with embedded sensors and their corresponding locations, the ARHMAM first mines activity data from web, and uses these to build a Hidden Markov Model (HMM). In comparison with the existing web data based AR systems, it has the following advantages: (1) it uses more strong activity model, (2) it reduces the mining time significantly. It is observed that the class accuracy of activity recognition of our system for a real-world dataset is more than 64%, which is 20% more in comparison with its counterpart. Additionally, the mining time complexity is far better than its counterpart.

[1]  Kent Larson,et al.  Using a Live-In Laboratory for Ubiquitous Computing Research , 2006, Pervasive.

[2]  Matthai Philipose,et al.  Unsupervised Activity Recognition Using Automatically Mined Common Sense , 2005, AAAI.

[3]  Matthai Philipose,et al.  Mining models of human activities from the web , 2004, WWW '04.

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

[5]  Dieter Fox,et al.  Location-Based Activity Recognition , 2005, KI.

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

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

[8]  Martha E. Pollack,et al.  An 'Object-Use Fingerprint': The Use of Electronic Sensors for Human Identification , 2007, UbiComp.

[9]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[10]  Qiang Yang,et al.  Real world activity recognition with multiple goals , 2008, UbiComp.

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

[12]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[13]  Qiang Yang,et al.  CIGAR: Concurrent and Interleaving Goal and Activity Recognition , 2008 .

[14]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.