An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences
Joint Authors
Liang, Xiaohui
Liu, Jie
Fan, Xiaohai
Guo, Chengyu
Qin, Aihong
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-10
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors.
The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs.
Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model.
Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data.
In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results.
Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task.
The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.
American Psychological Association (APA)
Guo, Chengyu& Liu, Jie& Fan, Xiaohai& Qin, Aihong& Liang, Xiaohui. 2014. An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-452766
Modern Language Association (MLA)
Guo, Chengyu…[et al.]. An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences. Mathematical Problems in Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-452766
American Medical Association (AMA)
Guo, Chengyu& Liu, Jie& Fan, Xiaohai& Qin, Aihong& Liang, Xiaohui. An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-452766
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-452766