This paper proposes a novel parallel structure and an individual multibehavior module which is based on hidden Markov models (HMM) to extract behavior features. In the parallel structure, the wavelet de-noising method is employed to preprocess data and provide the robust training data. Then, the individual multi-behavior module is built to analyze multi-sensor signals to obtain the behavior features. The experimental results show that this method is useful for behavior understanding such as sleeping, for which the matched probability is up to 90%.
|Number of pages||6|
|Journal||ICIC Express Letters, Part B: Applications|
|Publication status||Published - 2012 Dec 13|
- Behavior understanding
- Wavelet denoising
ASJC Scopus subject areas
- Computer Science(all)