research-article

American Static Signs Recognition Using Leap Motion Sensor

Authors:

Rajesh B. Mapari,

Govind KharatAuthors Info & Claims

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

Article No.: 67, Pages 1 - 5

https://doi.org/10.1145/2905055.2905125

Published: 04 March 2016 Publication History

Abstract

Advancement in technology has opened new ways in the field of Human Machine Interaction. A Novel method for recognition of American Sign Language (ASL) is proposed using Leap Motion Sensor. Static signs (A to Z and numbers from 1 to 10) excluding J and Z are used for processing. However 2 and 6 also excluded from dataset as the posture of these is similar to V and W respectively. Features Set consist of positional values (fingers and palm), distance and angle values. Total 48 features are used to recognize ASL using Multilayer Perceptron (MLP) which is a feed forward artificial neural network. Dataset consists of 146 users who have performed 32 signs resulting in total dataset of 4672 signs. Out of this 90% dataset is used for training and 10% dataset is used for CV (Cross Validation)/testing. The average classification accuracy obtained is near about 90%.

References

[1]

Vasiliki E. Kosmidou, Leontios J. Hadjileontiadis, Stavros M. Panas. 2006. Evaluation of surface EMG features for the recognition of American Sign Language gestures. Proceedings of the 28th IEEE EMBS Annual International Conference (New York City, USA, Aug. 30-Sept. 3, 2006), 6197--6200. DOI = http://10.1109/IEMBS.2006.259428.

[2]

Hee-Deok Yang, Seong-Whan Lee. 2011. Combination of Manual and Non-Manual Features for Sign Language Recognition Based on Conditional Random Field and Active Appearance Model. In Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (Guilin, July 10-13, 2011), 1726--1731. DOI = http://10.1109/IMLC.2011.6016973.

[3]

Fu-Hua Chou, Yung-Chun Su. 2012. An Encoding and Identification Approach for the Static Sign Language Recognition. The 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Kaohsiung, Taiwan, July 11-14, 2012) 885--889. DOI = http://10.1109/AIM.2012.6266025.

[4]

M.S. Sinith, Soorej G Kamal, Nisha B., Nayana S, Kiran Surendran, Jith P S. 2012. Sign Language Recognition Using Support Vector Machine. In International Conference on Advances in Computing and Communications (Cochin, Kerala, Aug. 9-11, 2012). 122--125. DOI = http://10.1016/j.csl.2012.06.003.

Digital Library

[5]

Fahad Ullah. 2011. American Sign Language Recognition System for Hearing Impaired People Using Cartesian Genetic Programming. 5th International Conference on Robotics and Application (Wellington, Dec. 6-8, 2011). 96--99. DOI = http://10.1109/ICARA.2012.6144863.

[6]

Asha Thalange, Dr.Shantanu Dixit. 2015. Effect of Thinning Extent on ASL Number Recognition Using Open-finger Distance Feature Measurement Technique. International Conference on Signal Processing And Communication Engineering Systems (SPACES) (Guntur, Jan. 2-3, 2015). 39--43. DOI= http://10.1109/SPACES.2015.7058299.

[7]

Priyanka Mekala, Ying Gao, et.al. 2011. Real-time Sign Language Recognition based on Neural Network Architecture. In proceeding of 43rd IEEE Southeastern Symposium on System Theory (SSST) (Auburn, AL, March 14-16, 2011). 195--199. DOI= http://10.1109/SSST.2011.5753805.

[8]

Dominique Uebersax, Juergen Gall et al. 2011. Real-time Sign Language Letter and Word Recognition from Depth Data. IEEE International Conference on Computer Vision Workshops (Barcelona, Nov. 6-13, 2011). 383--390. DOI = http://10.1109/ICCVW.2011.6130267.

[9]

C. S. Weerasekera, M. H. Jaward, N. Kamrani. 2013. Robust ASL Fingerspelling Recognition Using Local Binary Patterns And Geometric Features. International Conference on Digital Image Computing: Techniques and Applications (DICTA) (Hobart, TAS, Nov. 26-28, 2013). 1--8. DOI = http://10.1109/DICTA.2013.6691521.

[10]

L. Nanni, A. Lumini, F. Dominio, M. Donadeo, and P. Zanuttigh. 2014. Ensemble to improve gesture recognition. International Journal of Automated Identification Technology.

[11]

Saad Masood et al. 2014. Dynamic Time Wrapping based Gesture Recognition. International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE) (Islamabad, Pakistan, April 22-24, 2014). 205--210. DOI = http://10.1109/iCREATE.2014.6828366.

[12]

Cao Dong, Ming C. Leu, Zhaozheng Yin. 2015. American Sign Language Alphabet Recognition Using Microsoft Kinect. The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Boston, MA, June 7-12, 2015). 44--52. DOI = http://10.1109/CVPRW.2015.7301347.

[13]

Lucas Rioux, Philippe Giguère. 2014. Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network. Canadian Conference on Computer and Robot Vision (Montreal, QC, May 6-9, 2014). 92--97. DOI = http://10.1109/CRV.2014.20.

Digital Library

[14]

Giulio Marin, Fabio Dominio, Pietro Zanuttigh. 2014. Hand gesture recognition with leap motion and kinect devices. IEEE International Conference on Image Processing (ICIP) (Paris, Oct. 27-30, 2014). 1565 --1569. DOI = http://10.1109/ICIP.2014.7025313

[15]

Makiko Funasaka, Yu Ishikawa, Masami Takata, and Kazuki Joe. 2015. Sign Language Recognition using Leap Motion Controller. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA). 263--269.

[16]

Wikipedia. "Leap Motion" Wikipedia.org. {Online}. Available https://en.wikipedia.org/wiki/Leap_Motion {Last Modified: 26 July 2015, 07:16}.

Cited By

Bhiri NAmeur SJegham IAlouani IBen Khalifa A(2024)2MLMD: Multi-modal Leap Motion Dataset for Home Automation Hand Gesture Recognition SystemsArabian Journal for Science and Engineering10.1007/s13369-024-09396-6Online publication date: 16-Aug-2024
https://doi.org/10.1007/s13369-024-09396-6
Elona JJayasree T(2024)Smart Gaming System for Hand RehabilitationBiomedical Engineering Science and Technology10.1007/978-3-031-54547-4_4(35-46)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-54547-4_4
Galván-Ruiz JTravieso-González CPinan-Roescher AAlonso-Hernández J(2023)Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame InformationSensors10.3390/s2301048123:1(481)Online publication date: 2-Jan-2023
https://doi.org/10.3390/s23010481
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American Static Signs Recognition Using Leap Motion Sensor
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cover image ACM Other conferences

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

March 2016

843 pages

ISBN:9781450339629

DOI:10.1145/2905055

Copyright © 2016 ACM.

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Published: 04 March 2016

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ICTCS '16

ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies

March 4 - 5, 2016

Udaipur, India

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Cited By

Bhiri NAmeur SJegham IAlouani IBen Khalifa A(2024)2MLMD: Multi-modal Leap Motion Dataset for Home Automation Hand Gesture Recognition SystemsArabian Journal for Science and Engineering10.1007/s13369-024-09396-6Online publication date: 16-Aug-2024
https://doi.org/10.1007/s13369-024-09396-6
Elona JJayasree T(2024)Smart Gaming System for Hand RehabilitationBiomedical Engineering Science and Technology10.1007/978-3-031-54547-4_4(35-46)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-54547-4_4
Galván-Ruiz JTravieso-González CPinan-Roescher AAlonso-Hernández J(2023)Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame InformationSensors10.3390/s2301048123:1(481)Online publication date: 2-Jan-2023
https://doi.org/10.3390/s23010481
Chophuk PChamnongthai KChinnasarn K(2022)Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship PatternsSensors10.3390/s2212455422:12(4554)Online publication date: 16-Jun-2022
https://doi.org/10.3390/s22124554
Sluÿters AOusmer MRoselli PVanderdonckt J(2022)QuantumLeap, a Framework for Engineering Gestural User Interfaces based on the Leap Motion ControllerProceedings of the ACM on Human-Computer Interaction10.1145/35322116:EICS(1-47)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1145/3532211
Suhas TTeja PRakesh NKumar DKishore P(2022)Skeletal Action Recognition with Local Joint Perimeter Maps Learned Using Deep Metric Embedding2022 IEEE Delhi Section Conference (DELCON)10.1109/DELCON54057.2022.9752983(1-5)Online publication date: 11-Feb-2022
https://doi.org/10.1109/DELCON54057.2022.9752983
Kumar DSastry AKishore PKumar E(2022)3D sign language recognition using spatio temporal graph kernelsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2018.11.00834:2(143-152)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2018.11.008
Alnajjar MAbutabikh AIssa ADebeljak MCikajlo I(2020)Development of 3D Exergame for Upper Limbs Rehabilitation Using Leap Motion Controller and Unity2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech)10.1109/iCareTech49914.2020.00011(24-29)Online publication date: Aug-2020
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Ameur SBen Khalifa ABouhlel M(2020)Chronological pattern indexing: An efficient feature extraction method for hand gesture recognition with Leap MotionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2020.10284270(102842)Online publication date: Jul-2020
https://doi.org/10.1016/j.jvcir.2020.102842
Alvarez-Robles TÁlvarez FCarreño-León M(2020)Proposal for an Interactive Software System Design for Learning Mexican Sign Language with Leap MotionHCI International 2020 – Late Breaking Papers: Universal Access and Inclusive Design10.1007/978-3-030-60149-2_15(184-196)Online publication date: 25-Sep-2020
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