open access

https://hdl.handle.net/2115/39816

Human Action Recognition Using Acceleration Information Based On Hidden Markov Model

英語

2009-10-04

Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, International Organizing Committee

Proceedings : APSIPA ASC 2009 : Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference

829-832

APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Poster session: Image, Video, and Multimedia Signal Processing 3 (7 October 2009).

This paper investigates the best feature parameter for human action recognition by using Hidden Markov Model (HMM) with triaxial acceleration sensor information. Our target is to recognize six types of basic actions (walk, stay, sit down, stand up, lie down, get up) in the room of daily life. First, as parameters in time domain, acceleration information in three axes and their derivatives are used as the baseline method. Secondly, Mel-Frequency Cepstral Coefficients are used as feature parameters in frequency domain. As the recognition result, the best recognition rate was obtained when MFCC and its Δ elements of the axis that contained most of gravitational acceleration information.

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