id:"swepub:oai:DiVA.org:kth-17769"
Search: id:"swepub:oai:DiVA.org:kth-17769" > Motion intention re...
- 1 of 1
- Previous record
- Next record
- To hitlist
- Aarno, DanielKTH,Datorseende och robotik, CVAP,Centrum för Autonoma System, CAS (author)
Motion intention recognition in robot assisted applications
- Article/chapterEnglish2008
Publisher, publication year, extent ...
- Elsevier BV,2008
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:kth-17769
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-17769URI
- https://doi.org/10.1016/j.robot.2007.11.005DOI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- QC 20100525 QC 20111214
- Acquiring, representing and modelling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. The problems are challenging mainly because of the lack of a general mathematical model to describe human skills. One of the common approaches is to divide the task that the operator is executing into several subtasks or low-level subsystems in order to provide manageable modelling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gesteme classifier that classifies motions into basic action-primitives, or gestemes. The gesteme classifiers are then used in a LHMM to model a teleoperated task. The proposed methodology uses three different HMM models at the gesteme level: one-dimensional HMM, multi-dimensional HMM and multidimensional HMM with Fourier transform. The online and off-line classification performance of these three models is evaluated with respect to the number of gestemes, the influence of the number of training samples, the effect of noise and the effect of the number of observation symbols. We also apply the LHMM to data recorded during the execution of a trajectory tracking task in 2D and 3D with a mobile manipulator in order to provide qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modelling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multidimensional HMMs for gesteme classification is small. It can also be seen that the LHMM is robust with respect to misclassifications in the underlying gesteme classifiers.
Subject headings and genre
- Layered Hidden Markov Models
- human-machine collaboration
- motion
- intention recognition
Added entries (persons, corporate bodies, meetings, titles ...)
- Kragic, DanicaKTH,Datorseende och robotik, CVAP,Centrum för Autonoma System, CAS(Swepub:kth)u1ydsyln (author)
- KTHDatorseende och robotik, CVAP (creator_code:org_t)
Related titles
- In:Robotics and Autonomous Systems: Elsevier BV56:8, s. 692-7050921-88901872-793X
Internet link
Find in a library
- Robotics and Autonomous Systems (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Aarno, Daniel
- Kragic, Danica
- Articles in the publication
- Robotics and Aut ...
- By the university
- Royal Institute of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
