Title

Motif Discovery And Feature Selection For Crf-Based Activity Recognition

Keywords

Activity recognition; CRF; Feature selection

Abstract

Due to their ability to model sequential data without making unnecessary independence assumptions, conditional random fields (CRFs) have become an increasingly popular discriminative model for human activity recognition. However, how to represent signal sensor data to achieve the best classification performance within a CRF model is not obvious. This paper presents a framework for extracting motif features for CRF-based classification of IMU (inertial measurement unit) data. To do this, we convert the signal data into a set of motifs, approximately repeated symbolic subsequences, for each dimension of IMU data. These motifs leverage structure in the data and serve as the basis to generate a large candidate set of features from the multi-dimensional raw data. By measuring reductions in the conditional log-likelihood error of the training samples, we can select features and train a CRF classifier to recognize human activities. An evaluation of our classifier on the CMU Multi-Modal Activity Database reveals that it outperforms the CRF-classifier trained on the raw features as well as other standard classifiers used in prior work. © 2010 IEEE.

Publication Date

11-18-2010

Publication Title

Proceedings - International Conference on Pattern Recognition

Number of Pages

3826-3829

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICPR.2010.932

Socpus ID

78149473878 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/78149473878

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