Stacked Hidden Markov model for motion intention recognition

Tao Yang, Weimin Huang, Zhenhua Jiang, Chee Kong Chui, Liu Jiang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages266-271
Number of pages6
ISBN (Electronic)9781538609682
DOIs
Publication statusPublished - 29 Nov 2017
Externally publishedYes
Event2nd IEEE International Conference on Signal and Image Processing, ICSIP 2017 - Singapore, Singapore
Duration: 4 Aug 20176 Aug 2017

Publication series

Name2017 IEEE 2nd International Conference on Signal and Image Processing, ICSIP 2017
Volume2017-January

Conference

Conference2nd IEEE International Conference on Signal and Image Processing, ICSIP 2017
Country/TerritorySingapore
CitySingapore
Period4/08/176/08/17

Keywords

  • Hidden markov models
  • Laparoscopes
  • Medical robotics
  • Motion intention recognition
  • Surgical simulation

ASJC Scopus subject areas

  • Signal Processing

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