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Gesture recognition with 3D sensors using Hidden Markov Models and clustering

  • We propose a method for recognizing dynamic gestures using a 3D sensor. New aspects of the developed system include problem-adapted data conversion and compression as well as automatic detection of different variants of the same gesture via clustering with a suitable metric inspired by Jaccard metric. The combination of Hidden Markov Models and clustering leads to robust detection of different executions based on a small set of training data. We achieved an increase of 5% recognition rate compared to regular Hidden Markov Models. The system has been used for human-machine interaction and might serve as an assistive system in physiotherapy and neurological or orthopedic diagnosis.

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Metadaten
Name:Priwitzer, Barbara
DOI:https://doi.org/10.1109/IWOBI47054.2019.9114513
Erschienen in:IWOBI 2019 : IEEE International Work Conference on Bioinspired Intelligence : proceeding, July 3-5, 2019, Budapest, Hungary
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Tag:HMM; clustering; depth sensor; gesture
Pagenumber:6
First Page:127
Last Page:132
Dewey Decimal Classification:004 Informatik
Open Access:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE