research-article

Visualization of Grasping Operations based on Hand Kinematics measured through Data Glove

Authors:

Manalee Dev Sharma,

Nabasmita Phukan,

Nayan M. Kakoty,

Durlav SonowalAuthors Info & Claims

AIR '17: Proceedings of the 2017 3rd International Conference on Advances in Robotics

Article No.: 41, Pages 1 - 6

https://doi.org/10.1145/3132446.3134905

Published: 28 June 2017 Publication History

Abstract

Although a number of prosthetic hands have been reported, anthropomorphic control is still a challenge. Precise determination of human hand kinematics will certainly enhance the control for prosthetic hands. One of the ways to push the research forward is to measure and visualize the human hand kinematics in real-time during grasping operations. This paper reports the development of a data glove that can measure human hand finger joint kinematics. The measured hand kinematics is visualized for 16 grasp types, adopted from Cutkosky's grasps taxonomy, in SynGrasp MATLAB toolbox. The glove can measure the finger joint angles with an accuracy±standard deviation for metacarpophalangeal (MCP)±4°, proximal inter phalangeal (PIP)±2° and distal inter phalangeal (DIP)±2° during flexion/ extension and abduction/ adduction.

References

[1]

A. Miller, P. Allen, V. S., and Valero-Cuevas, F. From robotic hands to human hands: a visualization and simulation engine for grasping research. Industrial Robot 32, 1 (2005), 55--63.

[2]

Assaleh, K., Shanableh, T., Fanaswala, M., Amin, F., and Bajaj, H. Continuous arabic sign language recognition in user dependent mode. Intelligent Learning Systems and Applications 2, 1 (2010), 19--27.

[3]

Burdea, G. C., and Coiffet, P. Virtual Reality Technology. Wiley, 2003.

Digital Library

[4]

Cao, X., and Cleghorn, W. L. Examples and application of adams software in the mechanics of machines teaching. In 5th Intl. Conf. on Computer Science and Education (China, 2010), pp. 1637--1641.

[5]

Corke, P. A robotics toolbox for matlab. Tech. rep., IEEE Robotics and Automation Magazine, 1996.

[6]

Cutkosky, M. R. On grasp choice, grasp models and the design of hands for manufacturing tasks. Robotics and Automation 5, 3 (1989), 269--278.

[7]

Dassault Systemes. Solutions Portfolio - Virtual Manufacturing for Real Savings, 2002.

[8]

Dipietro, L., Sabatini, A. M., and Dario, P. A survey of glove-based systems and their applications. IEEE Transactions on Systems, Man, and Cybernetics 38, 4 (2008), 461--482.

Digital Library

[9]

Gray, H. Anatomy of the Human Body. Lea and Febiger, Philadelphia, 1918.

[10]

Johari, N. A. M., Haron, H., and Jaya, A. S. M. Robotic modeling and simulation of palletizer robot using workspace5. In Computer Graphics, Imaging and Visualisation (Thailand, 2007), pp. 217--222.

Digital Library

[11]

Kuroda, T., Tabata, Y., Goto, A., Ikuta, H., and Murakami, M. Consumer price data-glove for sign language recognition. In Proceedings of the 5th International Conference on Disability, Virtual Reality and Associated Technologies (UK, 2004), pp. 253--258.

[12]

LaViola, J. J. A survey of hand posture and gesture recognition techniques and technology. Tech. rep., Brown University Providence, 1999.

Digital Library

[13]

Leon, B., Ulbrich, S., Diankov, R., Puche, G., Przybylski, M., Morales, A., Asfour, T., Moisio, S., Bohg, J., Kuffner, J., and Dillmann, R. Opengrasp: A toolkit for robot grasping simulation. In Intl. Conf. on Simulation, Modeling, and Prog. for Auto. Robots (Germany, 2010), pp. 109--120.

Digital Library

[14]

Mehdi, S. A., and Khan, Y. N. Sign language recognition using sensor gloves. In Proceedings of the 9th International Conference on Neural Information Processing (Singapore, 2002), pp. 2204--2206.

[15]

Mulder, A. G. E., and Fels, S. S. Sound sculpting: Manipulating sound through virtual sculpting. In Proceedings of the 1998 Western Computer Graphics Symposium (1998), pp. 15--23.

[16]

Prattichizzo, D., Malvezzi, M., and Bicchi, A. On motion and force controllability of grasping hands with postural synergies. In Proceedings of Robotics: Science and Systems (Zaragoza, Spain, 2010), pp. 1--8.

[17]

Rajeevlochana, C. G., and Saha, S. K. Roboanalyzer: 3d model based robotic learning software. In International Conference on Multi Body Dynamics (Vijayawada, India, 2011), pp. 3--13.

[18]

Rijpkema, H., and Girard, M. Computer animation of knowledge-based human grasping. In Proceedings of the 18th annual conference on Computer graphics and interactive techniques (Las Vegas, USA, 1991), pp. 339--348.

Digital Library

[19]

Speck, A., and Klaeren, H. Robosim: Java 3d robot visualization. In 25th Conf. of IEEE Industrial Electronics Society (USA, 1999), pp. 821--826.

[20]

Veltink, P. H., Kortier, H. G., Schepers, H. M., Sluiter, V. I., Brookhuis, R. A., Lammerink, T. S. J., and Wiegerink, R. J. Powerglove, concepts and current results. In XII Intl. Symposium on 3D Analysis of Human Movement (Italy, 2012), pp. 42--45.

[21]

Voldman, J., Gray, M. L., and Schmidt, M. A. Microfabrication in biology and medicine. Annual Review of Biomedical Engineering 1, 401 (1999), 425.

[22]

Zimmerman, T. G. Optical flex sensor, 1982. U.S. Patent 4 542 291.

Cited By

Millar CSiddique NKerr E(2021)LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor2021 7th International Conference on Control, Automation and Robotics (ICCAR)10.1109/ICCAR52225.2021.9463477(213-222)Online publication date: 23-Apr-2021
https://doi.org/10.1109/ICCAR52225.2021.9463477

Recommendations

Interactive hand pose estimation using a stretch-sensing soft glove

We propose a stretch-sensing soft glove to interactively capture hand poses with high accuracy and without requiring an external optical setup. We demonstrate how our device can be fabricated and calibrated at low cost, using simple tools available in ...
Evaluation of hand grasping by instrumented glove

In this paper the evaluation of effectivenes of instrumented glove in measurement of hand opening and preshaping during reach-to-grasp movement is described. In the experiment presented in this work the reach-to-grasp movement of one healthy subject was ...
Glove-based virtual hand grasping for virtual mechanical assembly

PurposeHand gesture-based interaction can provide far more intuitive, natural and immersive feelings for users to manipulate 3D objects for virtual assembly VA. A mechanical assembly consists of mostly general-purpose machine elements or mechanical ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

AIR '17: Proceedings of the 2017 3rd International Conference on Advances in Robotics

June 2017

325 pages

ISBN:9781450352949

DOI:10.1145/3132446

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

IIT-Delhi: IIT-Delhi

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

AIR '17

AIR '17: Advances in Robotics

June 28 - July 2, 2017

New Delhi, India

Acceptance Rates

Overall Acceptance Rate 69 of 140 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
157
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Millar CSiddique NKerr E(2021)LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor2021 7th International Conference on Control, Automation and Robotics (ICCAR)10.1109/ICCAR52225.2021.9463477(213-222)Online publication date: 23-Apr-2021
https://doi.org/10.1109/ICCAR52225.2021.9463477

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents