Motif Discovery and Feature Selection for CRF-based Activity Recognition

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 sub sequences, 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.