论文信息 - Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker

Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker

Rollating walkers are popular mobility aids used by older adults to improve balance control. There is a need to automatically recognize the activities performed by walker users to better understand activity patterns, mobility issues and the context in which falls are more likely to happen. We design and compare several techniques to recognize walker related activities. A comprehensive evaluation with control subjects and walker users from a retirement community is presented.

[1] Andrew McCallum,et al. An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[2] M. Alwan,et al. Passive derivation of basic walker-assisted gait characteristics from measured forces and moments , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3] James Tung,et al. Poster 87: Frontal Plane Balance Control With Rollators: Perturbed Stance and Walking , 2008 .

[4] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[5] Henry A. Kautz,et al. Learning and inferring transportation routines , 2004, Artif. Intell..

[6] Jun S. Liu,et al. The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .

[7] Ben Taskar,et al. An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[8] Kazuhiro Kosuge,et al. Motion control of intelligent passive-type Walker for fall-prevention function based on estimation of user state , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9] Henry A. Kautz,et al. Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[10] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.