IEICE TRANSACTIONS on Information
Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera
Summary :
This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.2 pp.319-330
- Publication Date
- 2019/02/01
- Publicized
- 2018/11/12
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7146
- Type of Manuscript
- PAPER
- Category
- Human-computer Interaction
Authors
Longfei CHEN
Kyoto University
Yuichi NAKAMURA
Kyoto University
Kazuaki KONDO
Kyoto University
Walterio MAYOL-CUEVAS
University of Bristol
Keyword
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Longfei CHEN, Yuichi NAKAMURA, Kazuaki KONDO, Walterio MAYOL-CUEVAS, "Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 2, pp. 319-330, February 2019, doi: 10.1587/transinf.2018EDP7146.
Abstract: This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7146/_p
Copy
@ARTICLE{e102-d_2_319,
author={Longfei CHEN, Yuichi NAKAMURA, Kazuaki KONDO, Walterio MAYOL-CUEVAS, },
journal={IEICE TRANSACTIONS on Information},
title={Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera},
year={2019},
volume={E102-D},
number={2},
pages={319-330},
abstract={This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.},
keywords={},
doi={10.1587/transinf.2018EDP7146},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera
T2 - IEICE TRANSACTIONS on Information
SP - 319
EP - 330
AU - Longfei CHEN
AU - Yuichi NAKAMURA
AU - Kazuaki KONDO
AU - Walterio MAYOL-CUEVAS
PY - 2019
DO - 10.1587/transinf.2018EDP7146
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2019
AB - This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.
ER -
English
