Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble

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LUŽANIN, Ognjan ;PLANČAK, Miroslav .
Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 55, n.4, p. 230-236, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/>. Date accessed: 29 oct. 2020. 
doi:http://dx.doi.org/.
Lužanin, O., & Plančak, M.
(2009).
Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble.
Strojniški vestnik - Journal of Mechanical Engineering, 55(4), 230-236.
doi:http://dx.doi.org/
@article{.,
	author = {Ognjan  Lužanin and Miroslav  Plančak},
	title = {Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {55},
	number = {4},
	year = {2009},
	keywords = {virtual reality; data glove; static gesture; artificial neural network; },
	abstract = {This paper focuses on the enhancement of the static gesture dictionary of commercial data glove 5DT 5 Ultra, with the primary goal to improve its ergonomic features and usability for Mechanical CAD (MCAD). The standard gesture dictionary of this data glove is based on 16 joint-limit simple static gestures designed in the 1990s by the NASA Aimes laboratory. Although simple to learn and perform and easy to recognize, most of these gestures have poor ergonomic features and are non-intuitive in the symbolical sense. The authors addressed this problem and suggested improvements by eliminating 11 original simple static gestures and substituting them with new complex static gestures. Since the restructured gesture dictionary of 12 simple and complex static gestures imposed a problem of lower gesture recognition rate, this issue was approached using artificial intelligence. Namely, an ensemble of five multilayer perceptrons (MLPs) with backpropagation was used as gesture classifier. Bearing in mind that variable hand anatomies of different data glove users are one of the crucial factors impeding gesture recognition, two female and three male subjects participated in the gesture data acquisition to provide a total of 2400 static gestures which were used to train, validate and test the ensemble classifier. For each of the five member networks, a resampling of the data set was performed, aleviating the problem of variance. The results showed that the proposed restructuring of data dictionary can be efficiently supported by the ensemble-based gesture classifier.},
	issn = {0039-2480},	pages = {230-236},	doi = {},
	url = {https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/}
}
Lužanin, O.,Plančak, M.
2009 August 55. Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 55:4
%A Lužanin, Ognjan 
%A Plančak, Miroslav 
%D 2009
%T Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble
%B 2009
%9 virtual reality; data glove; static gesture; artificial neural network; 
%! Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble
%K virtual reality; data glove; static gesture; artificial neural network; 
%X This paper focuses on the enhancement of the static gesture dictionary of commercial data glove 5DT 5 Ultra, with the primary goal to improve its ergonomic features and usability for Mechanical CAD (MCAD). The standard gesture dictionary of this data glove is based on 16 joint-limit simple static gestures designed in the 1990s by the NASA Aimes laboratory. Although simple to learn and perform and easy to recognize, most of these gestures have poor ergonomic features and are non-intuitive in the symbolical sense. The authors addressed this problem and suggested improvements by eliminating 11 original simple static gestures and substituting them with new complex static gestures. Since the restructured gesture dictionary of 12 simple and complex static gestures imposed a problem of lower gesture recognition rate, this issue was approached using artificial intelligence. Namely, an ensemble of five multilayer perceptrons (MLPs) with backpropagation was used as gesture classifier. Bearing in mind that variable hand anatomies of different data glove users are one of the crucial factors impeding gesture recognition, two female and three male subjects participated in the gesture data acquisition to provide a total of 2400 static gestures which were used to train, validate and test the ensemble classifier. For each of the five member networks, a resampling of the data set was performed, aleviating the problem of variance. The results showed that the proposed restructuring of data dictionary can be efficiently supported by the ensemble-based gesture classifier.
%U https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/
%0 Journal Article
%R 
%& 230
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 55
%N 4
%@ 0039-2480
%8 2017-08-21
%7 2017-08-21
Lužanin, Ognjan, & Miroslav  Plančak.
"Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble." Strojniški vestnik - Journal of Mechanical Engineering [Online], 55.4 (2009): 230-236. Web.  29 Oct. 2020
TY  - JOUR
AU  - Lužanin, Ognjan 
AU  - Plančak, Miroslav 
PY  - 2009
TI  - Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - virtual reality; data glove; static gesture; artificial neural network; 
N2  - This paper focuses on the enhancement of the static gesture dictionary of commercial data glove 5DT 5 Ultra, with the primary goal to improve its ergonomic features and usability for Mechanical CAD (MCAD). The standard gesture dictionary of this data glove is based on 16 joint-limit simple static gestures designed in the 1990s by the NASA Aimes laboratory. Although simple to learn and perform and easy to recognize, most of these gestures have poor ergonomic features and are non-intuitive in the symbolical sense. The authors addressed this problem and suggested improvements by eliminating 11 original simple static gestures and substituting them with new complex static gestures. Since the restructured gesture dictionary of 12 simple and complex static gestures imposed a problem of lower gesture recognition rate, this issue was approached using artificial intelligence. Namely, an ensemble of five multilayer perceptrons (MLPs) with backpropagation was used as gesture classifier. Bearing in mind that variable hand anatomies of different data glove users are one of the crucial factors impeding gesture recognition, two female and three male subjects participated in the gesture data acquisition to provide a total of 2400 static gestures which were used to train, validate and test the ensemble classifier. For each of the five member networks, a resampling of the data set was performed, aleviating the problem of variance. The results showed that the proposed restructuring of data dictionary can be efficiently supported by the ensemble-based gesture classifier.
UR  - https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/
@article{{}{.},
	author = {Lužanin, O., Plančak, M.},
	title = {Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {55},
	number = {4},
	year = {2009},
	doi = {},
	url = {https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/}
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TY  - JOUR
AU  - Lužanin, Ognjan 
AU  - Plančak, Miroslav 
PY  - 2017/08/21
TI  - Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 55, No 4 (2009): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - virtual reality, data glove, static gesture, artificial neural network, 
N2  - This paper focuses on the enhancement of the static gesture dictionary of commercial data glove 5DT 5 Ultra, with the primary goal to improve its ergonomic features and usability for Mechanical CAD (MCAD). The standard gesture dictionary of this data glove is based on 16 joint-limit simple static gestures designed in the 1990s by the NASA Aimes laboratory. Although simple to learn and perform and easy to recognize, most of these gestures have poor ergonomic features and are non-intuitive in the symbolical sense. The authors addressed this problem and suggested improvements by eliminating 11 original simple static gestures and substituting them with new complex static gestures. Since the restructured gesture dictionary of 12 simple and complex static gestures imposed a problem of lower gesture recognition rate, this issue was approached using artificial intelligence. Namely, an ensemble of five multilayer perceptrons (MLPs) with backpropagation was used as gesture classifier. Bearing in mind that variable hand anatomies of different data glove users are one of the crucial factors impeding gesture recognition, two female and three male subjects participated in the gesture data acquisition to provide a total of 2400 static gestures which were used to train, validate and test the ensemble classifier. For each of the five member networks, a resampling of the data set was performed, aleviating the problem of variance. The results showed that the proposed restructuring of data dictionary can be efficiently supported by the ensemble-based gesture classifier.
UR  - https://www.sv-jme.eu/article/enhancing-gesture-dictionary-of-a-commercial-data-glove-using-complex-static-gestures-and-an-mlp-ensemble/
Lužanin, Ognjan, AND Plančak, Miroslav.
"Enhancing Gesture Dictionary of a Commercial Data Glove Using Complex Static Gestures and an MLP Ensemble" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 55 Number 4 (21 August 2017)

Authors

Affiliations

  • Faculty of Technical Sciences, Novi Sad, Serbia
  • Faculty of Technical Sciences, Novi Sad, Serbia

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 55(2009)4, 230-236

This paper focuses on the enhancement of the static gesture dictionary of commercial data glove 5DT 5 Ultra, with the primary goal to improve its ergonomic features and usability for Mechanical CAD (MCAD). The standard gesture dictionary of this data glove is based on 16 joint-limit simple static gestures designed in the 1990s by the NASA Aimes laboratory. Although simple to learn and perform and easy to recognize, most of these gestures have poor ergonomic features and are non-intuitive in the symbolical sense. The authors addressed this problem and suggested improvements by eliminating 11 original simple static gestures and substituting them with new complex static gestures. Since the restructured gesture dictionary of 12 simple and complex static gestures imposed a problem of lower gesture recognition rate, this issue was approached using artificial intelligence. Namely, an ensemble of five multilayer perceptrons (MLPs) with backpropagation was used as gesture classifier. Bearing in mind that variable hand anatomies of different data glove users are one of the crucial factors impeding gesture recognition, two female and three male subjects participated in the gesture data acquisition to provide a total of 2400 static gestures which were used to train, validate and test the ensemble classifier. For each of the five member networks, a resampling of the data set was performed, aleviating the problem of variance. The results showed that the proposed restructuring of data dictionary can be efficiently supported by the ensemble-based gesture classifier.

virtual reality; data glove; static gesture; artificial neural network;