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Online human in-hand manipulation skill recognition and learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

This work intends to contribute to transfer human in-hand manipulation skills to a dexterous prosthetic hand. We proposed a probabilistic framework for both human skill representation and high efficient recognition. Gaussian Mixture Model (GMM) as a probabilistic model, is highly applicable in clustering, data fitting and classification. The human in-hand motions were perceived by a wearable data glove, CyberGlove, the motion trajectory data proposed and represented by GMMs. Firstly, only a certain amount of motion data were used for batch learning the parameters of GMMs. Then, the newly coming data of human motions will help to update the parameters of the GMMs without observation of the historical training data, through our proposed incremental parameter estimation framework. Recognition in the research takes full advantages of the probabilistic model, when the GMMs were trained, the log-likelihood of a candidate trajectory can be used as a measurement to achieve human in-hand manipulation skill recognition. The recognition results of the online trained GMMs show a steady increase in accuracy, which proved that the incremental learning process improved the performance of human in-hand manipulation skill recognition.
Original languageEnglish
Title of host publicationTAROS 2019: Towards Autonomous Robotic Systems
Subtitle of host publication20th Annual Conference, TAROS 2019, London, UK, July 3–5, 2019, Proceedings, Part II
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
PublisherSpringer
Chapter10
Pages113-122
Number of pages10
ISBN (Electronic)978-3-030-25332-5
ISBN (Print)978-3-030-25331-8
DOIs
Publication statusPublished - 1 Aug 2019
Event20th Annual Conference on Towards Autonomous Robotic Systems - London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
Volume11650

Conference

Conference20th Annual Conference on Towards Autonomous Robotic Systems
Abbreviated titleTAROS 2019
CountryUnited Kingdom
CityLondon
Period3/07/195/07/19

Documents

  • OnlineHuman

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in TAROS 2019: Towards Autonomous Robotic Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-25332-5_10.

    Accepted author manuscript (Post-print), 3.43 MB, PDF document

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