Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer

Recently, thanks to a variety of sensors equipped on smartphone, a lot of research about mobile activity recognition using accelerometer have been studied for context inference of mobile user and healthcare applications. Previous works, however, have a limitation in classifying some activities because of intra-class variations and inter-class similarities. To handle this problem, in this paper we propose a novel method to recognize activity of smart phone user based on hidden Markov model, where an ensemble method of hidden Markov models is proposed and used to recognize activity. To evaluate our method, we have carried out some experiments by using UCI Human Activity Recognition dataset, and as a result we have achieved about 83.51% accuracy when using two simple features, mean and standard deviation. It is a comparable result to other powerful discriminative methods such as support vector machine and multilayer perceptron.