Abstract
This work intends to contribute to transfer human in-hand manipulation skills to a dexterous prosthetic hand. We proposed a probabilistic framework for both human skill representation and high efficient recognition. Gaussian Mixture Model (GMM) as a probabilistic model, is highly applicable in clustering, data fitting and classification. The human in-hand motions were perceived by a wearable data glove, CyberGlove, the motion trajectory data proposed and represented by GMMs. Firstly, only a certain amount of motion data were used for batch learning the parameters of GMMs. Then, the newly coming data of human motions will help to update the parameters of the GMMs without observation of the historical training data, through our proposed incremental parameter estimation framework. Recognition in the research takes full advantages of the probabilistic model, when the GMMs were trained, the log-likelihood of a candidate trajectory can be used as a measurement to achieve human in-hand manipulation skill recognition. The recognition results of the online trained GMMs show a steady increase in accuracy, which proved that the incremental learning process improved the performance of human in-hand manipulation skill recognition.
Supported by grant of the EU Seventh Framework Programme (Grant No. 611391).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Yousef, H., Boukallel, M., Althoefer, K.: Tactile sensing for dexterous in-hand manipulation in robotics—a review. Sens. Actuators A Phys. 167(2), 171–187 (2011)
Schaal, S.: Learning from demonstration. In: Advances in Neural Information Processing Systems, pp. 1040–1046 (1997)
Ju, Z., Liu, H.: Human hand motion analysis with multisensory information. IEEE/ASME Trans. Mechatron. 19(2), 456–466 (2014)
Chen, D., et al.: An interactive image segmentation method in hand gesture recognition. Sensors 17(2), 253 (2017)
Zhang, Y., Chen, L., Ran, X.: Online incremental EM training of GMM and its application to speech processing applications. In: Signal Processing (ICSP), 2010 IEEE 10th International Conference on Proceeding, pp. 1309–1312. IEEE (2010)
Sudsang, A., Srinivasa, N.: Grasping and in-hand manipulation: geometry and algorithms. Algorithmica 26(3–4), 466–493 (2000)
Kondo, M., Ueda, J., Ogasawara, T.: Recognition of in-hand manipulation using contact state transition for multifingered robot hand control. Robot. Auton. Syst. 56(1), 66–81 (2008)
Kumar, P., Verma, J., Prasad, S.: Hand data glove: a wearable real-time device for human-computer interaction. Int. J. Adv. Sci. Technol. 43 (2012)
Bendels, G. H., Kahlesz, F., Klein, R.: Towards the next generation of 3D content creation. In: Proceedings of the working conference on Advanced visual interfaces, pp. 283–289. ACM (2004)
de La Gorce, M., Paragios, N., Fleet, D. J.: Model-based hand tracking with texture, shading and self-occlusions. In: Computer Vision and Pattern Recognition, 2008, CVPR 2008, IEEE Conference On Proceeding, pp. 1–8. IEEE (2008)
Fukuda, O., Tsuji, T., Kaneko, M., Otsuka, A.: A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Trans. Robot. Autom. 19(2), 210–222 (2003)
Reddy, N.P., Gupta, V.: Toward direct biocontrol using surface EMG signals: control of finger and wrist joint models. Med. Eng. Phys. 29(3), 398–403 (2007)
Kuroda, T., Tabata, Y., Goto, A., Ikuta, H., Murakami, M.: Consumer price data-glove for sign language recognition. In: Proceedings of 5th International Conference on Disability, Virtual Reality Associated Technologies, Oxford, UK, pp. 253–258 (2004)
Fahn, C.S., Sun, H.: Development of a data glove with reducing sensors based on magnetic induction. IEEE Trans. Ind. Electron. 52(2), 585–594 (2005)
Calinon, S., Guenter, F., Billard, A.: On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 37(2), 286–298 (2007)
Kalgaonkar, K., Raj, B.: One-handed gesture recognition using ultrasonic Doppler sonar. In: Acoustics, Speech and Signal Processing, 2009, ICASSP 2009, IEEE International Conference on Proceeding, pp. 1889–1892. IEEE (2009)
Hall, P., Marshall, D., Martin, R.: Merging and splitting eigenspace models. IEEE Trans. Pattern Anal. Mach. Intell. 22(9), 1042–1049 (2000)
Song, M., Wang, H.: Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering. In: Intelligent Computing: Theory and Applications III, vol. 5803, pp. 174–184. International Society for Optics and Photonics (2005)
Hicks, Y., Hall, P.M., Marshall, D.: A method to add Hidden Markov Models with application to learning articulated motion. In: BMVC, pp. 1–10 (2003)
Ju, Z., Liu, H.: A unified fuzzy framework for human-hand motion recognition. IEEE Trans. Fuzzy Syst. 19(5), 901–913 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, D., Ju, Z., Zhou, D., Li, G., Liu, H. (2019). Online Human In-Hand Manipulation Skill Recognition and Learning. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-25332-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25331-8
Online ISBN: 978-3-030-25332-5
eBook Packages: Computer ScienceComputer Science (R0)