NDLI: A method for preventing accidents due to human action slip utilizing HMM-based Dempster-Shafer theory

Content Provider	IEEE Xplore Digital Library
Author	Yoji, Y. Tetsuya, M. Yoji, U. Yukitaka, S.
Copyright Year	2003
Description	Author affiliation: Toyota Technol. Inst., Nagoya, Japan (Yoji, Y.; Tetsuya, M.; Yoji, U.)
Abstract	We propose a method for preventing hazardous accidents due to operator's action slip in their use of skill-assist, which has been already introduced as a power assist device in automobile assembly processes. First, we show that hidden Markov model (HMM) can be used to detect human erroneous operation from data sequences of an operator's hand motion trajectory, but that a problem arises in direct application of HMM to human error detection when trajectory is not in alignment with any of pretaught trajectories. However, HMM-based Dempster-Shafer theory proposed in the study allows the system to select their safety- or productivity- oriented robot control policy, solves the detection problem, and performs secure accident prevention, Second, a workability improvement process which comprises a state-policy and a teach data renewal sub-processes allows for optimal reconstruction of the policy determinant state space and HMMs. Finally, experimental results verify our overall proposal, and demonstrate the effectiveness of the workability improvement process.
Sponsorship	IEEE Robotics & Autom. Soc. Nat. Sci. Council, Taiwan, Ministr. Educ., Taiwan
Starting Page	1490
Ending Page	1496
File Size	495058
Page Count	7
File Format	PDF
ISBN	0780377362
ISSN	10504729
DOI	10.1109/ROBOT.2003.1241802
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-14
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Hidden Markov models Motion detection Workability Automobiles Assembly Robot control Accident prevention State-space methods Proposals
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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