@inproceedings{d3803350b6ed4bdc80e12b2a388afe8b,
title = "Stacked Hidden Markov model for motion intention recognition",
abstract = "Motion intention recognition plays an important role in robot-assisted applications. A Stacked Hidden Markov Model (HMM) method is proposed to enable the robot to recognize the intention of a human user based on his/her motion trajectories. The Stacked HMM method is constructed based on the relationship of the observed objects. The motion intention recognition model contains multiple HMMs. Each HMM represents one motion intention in the corresponding level. Motion trajectories were collected from a Virtual Reality based surgical training platform. A two-Layered Stacked HMM intention recognition model has been built to recognize the motion intention in primitive level and subtask level. With the proposed intention recognition method, intention recognition rate for the primitive and subtask levels are 95.0±3.5% and 71.0±13.6% respectively. The proposed method is effective in the recognition of user's intention from different levels with motion trajectory.",
keywords = "Hidden markov models, Laparoscopes, Medical robotics, Motion intention recognition, Surgical simulation",
author = "Tao Yang and Weimin Huang and Zhenhua Jiang and Chui, {Chee Kong} and Liu Jiang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Signal and Image Processing, ICSIP 2017 ; Conference date: 04-08-2017 Through 06-08-2017",
year = "2017",
month = nov,
day = "29",
doi = "10.1109/SIPROCESS.2017.8124546",
language = "English",
series = "2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "266--271",
booktitle = "2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017",
address = "United States",
}