Integrating hidden Markov models based on Mixture of Templates and k NN2 ensemble for activity recognition
This paper considers the activity recognition problem using inertial sensor data. It is a challenging temporal pattern recognition problem as the sensor data can be easily mixed with noise and also has large intra class variation, resulting from different characteristic of people doing same activity, and interclass similarity among several similar activities. To handle these problems concentrating on the classification method, this paper proposes a novel ensemble scheme of hidden Markov models for activity recognition. To improve the performance of activity recognition, our method models the outputs of multiple hidden Markov models by using enhanced template based classifier fusion method, in which multiple local templates are generated as Mixture of Templates, and k NN2 ensemble method is proposed to elaborately recognize activities. To show the effectiveness of the proposed method, we carried out several experiments on UCI Human Activity Recognition dataset and compared our method with several alternative methods. As a result, our method outperforms other methods.
2016 23rd International Conference on Pattern Recognition (ICPR)