Human activity recognition from wireless sensor network data: benchmark and software

Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world datasets. Our results can be used as a baseline for comparing the performance of other pattern recognition methods (both probabilistic and non-probabilistic). The datasets used in this chapter are made public, together with the source code of the probabilistic models used.

[1]  Juan Carlos Augusto,et al.  Designing Smart Homes, The Role of Artificial Intelligence , 2006, Designing Smart Homes.

[2]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

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

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

[5]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[6]  Kevin P. Murphy Hidden semi-Markov models ( HSMMs ) , 2002 .

[7]  Chris D. Nugent,et al.  Using Event Calculus for Behaviour Reasoning and Assistance in a Smart Home , 2008, ICOST.

[8]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

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

[10]  M. Ogawa,et al.  Analysis of activities of daily living in elderly people living alone: single-subject feasibility study. , 2004, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[11]  Christopher G. Atkeson,et al.  Assistive intelligent environments for automatic health monitoring , 2005 .

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

[14]  Gwenn Englebienne,et al.  UvA-DARE ( Digital Academic Repository ) Activity recognition using semi-Markov models on real world smart home datasets , 2010 .

[15]  Svetha Venkatesh,et al.  Efficient duration and hierarchical modeling for human activity recognition , 2009, Artif. Intell..

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

[17]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[18]  Enrico Vicario,et al.  A Visual Editor to Support the Use of Temporal Logic for ADL Monitoring , 2007, ICOST.

[19]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

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

[21]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[22]  Jorge Nocedal,et al.  Representations of quasi-Newton matrices and their use in limited memory methods , 1994, Math. Program..

[23]  Gregory D. Abowd,et al.  The Aware Home: A living laboratory for technologies for successful aging , 2002 .

[24]  Sajal K. Das,et al.  Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) , 2004 .

[25]  Svetha Venkatesh,et al.  Recognition of human activity through hierarchical stochastic learning , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[26]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[27]  Hanna M. Wallach,et al.  Efficient Training of Conditional Random Fields , 2002 .

[28]  Jeff A. Bilmes,et al.  Hierarchical Models for Activity Recognition , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.