Marginal filtering in large state spaces

We describe the marginal filter for activity recognition using symbolic models.The marginal filter allows fine-grained activity recognition using wearable sensors.We identify and discuss advantages over particle filters for symbolic models. Recognising everyday activities including information about the context requires to handle large state spaces. The usage of wearable sensors like six degree of freedom accelerometers increases complexity even more. Common approaches are unable to maintain an accurate belief state within such complex domains. We show how marginal filtering can overcome limitations of standard particle filtering and efficiently infer the context of actions. Symbolic models of human behaviour are used to recognise activities in two different settings with different state space sizes. Based on these scenarios we compare the marginal filter to the standard particle filter. An evaluation shows that the marginal filter performs comparably in small state spaces but outperforms the particle filter in large state spaces.

[1]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[2]  Manuel Blum,et al.  Time Bounds for Selection , 1973, J. Comput. Syst. Sci..

[3]  P. Bickel,et al.  Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems , 2008, 0805.3034.

[4]  Svetha Venkatesh,et al.  Recognising Behaviours of Multiple People with Hierarchical Probabilistic Model and Statistical Data Association , 2006, BMVC.

[5]  Bernt Schiele,et al.  A database for fine grained activity detection of cooking activities , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Bernt Schiele,et al.  Remember and transfer what you have learned - recognizing composite activities based on activity spotting , 2010, International Symposium on Wearable Computers (ISWC) 2010.

[7]  Hector Geffner,et al.  Goal Recognition over POMDPs: Inferring the Intention of a POMDP Agent , 2011, IJCAI.

[8]  Nando de Freitas,et al.  Toward Practical N2 Monte Carlo: the Marginal Particle Filter , 2005, UAI.

[9]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[10]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[11]  Henry A. Kautz,et al.  Location-Based Reasoning about Complex Multi-Agent Behavior , 2012, J. Artif. Intell. Res..

[12]  Chris L. Baker,et al.  Action understanding as inverse planning , 2009, Cognition.

[13]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[14]  Maria Fox,et al.  PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains , 2003, J. Artif. Intell. Res..

[15]  Peng Dai,et al.  Group Interaction Analysis in Dynamic Context$^{\ast}$ , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Gerhard Lakemeyer,et al.  Plan Recognition by Program Execution in Continuous Temporal Domains , 2012, KI.

[17]  Jesse Hoey,et al.  Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  J. Gregory Trafton,et al.  Accommodating Human Variability in Human-Robot Teams through Theory of Mind , 2011, IJCAI.

[19]  Jessica K. Hodgins,et al.  Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database , 2008 .

[20]  Daniel Jackson,et al.  Rapid specification and automated generation of prompting systems to assist people with dementia , 2011, Pervasive Mob. Comput..

[21]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[22]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[23]  Richard Denney,et al.  A comparison of the model-based & algebraic styles of specification as a basis for test specification , 1996, SOEN.

[24]  Thomas Kirste,et al.  Plan Synthesis for Probabilistic Activity Recognition , 2013, ICAART.

[25]  Abdenour Bouzouane,et al.  A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients , 2011 .

[26]  David C. Minnen,et al.  Propagation networks for recognition of partially ordered sequential action , 2004, CVPR 2004.

[27]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[28]  T. Kirste,et al.  Computational State Space Models for Activity and Intention Recognition. A Feasibility Study , 2014, PloS one.

[29]  Séverine Dubuisson,et al.  DBN-Based Combinatorial Resampling for Articulated Object Tracking , 2012, UAI.

[30]  P. Fearnhead,et al.  On‐line inference for hidden Markov models via particle filters , 2003 .

[31]  Ramakant Nevatia,et al.  Hierarchical multi-channel hidden semi Markov graphical models for activity recognition , 2013, Comput. Vis. Image Underst..

[32]  Jesse Hoey,et al.  Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process , 2010, Comput. Vis. Image Underst..

[33]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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