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2017 | OriginalPaper | Buchkapitel

Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction

verfasst von : Simone Ciotti, Edoardo Battaglia, Iason Oikonomidis, Alexandros Makris, Aggeliki Tsoli, Antonio Bicchi, Antonis A. Argyros, Matteo Bianchi

Erschienen in: Wireless Mobile Communication and Healthcare

Verlag: Springer International Publishing

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Abstract

In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.

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Vorheriges Kapitel Development of a Sustainable and Ergonomic Interface for the EMG Control of Prosthetic Hands

Nächstes Kapitel A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography

It is worth to mention that robotics research has leveraged upon neuroscientific insights on synergies to inform the design and control of artificial hands, see e.g. [14‐16].

Images and depth maps are captured at \(640\times 480@24\) bit and \(640\times 480@16\) bit, respectively.

Implementation available online at: http://cvrlcode.ics.forth.gr/handtracking/.

Ciotti, S., et al.: A synergy-based optimally designed sensing glove for functional grasp recognition. Sensors 16(6), 811 (2016)CrossRef

Sturman, D.J., et al.: A survey of glove-based input. IEEE Comput. Graphics Appl. 14(1), 30–39 (1994)MathSciNetCrossRef

Dipietro, L., et al.: A survey of glove-based systems and their applications. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(4), 461–482 (2008)CrossRef

Bianchi, M., et al.: Synergy-based hand pose sensing: Reconstruction enhancement. Int. J. Robot. Res. 32(4), 396–406 (2013)CrossRef

Oikonomidis, I., et al.: Efficient model-based 3D tracking of hand articulations using kinect. In: British Machine Vision Conference (BMVC 2011), vol. 1, no. 2, pp. 1–11. BMVA, Dundee (2011)

Muth, J.T., et al.: Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv. Mater. 26(36), 6307–6312 (2014)CrossRef

Hsiao, P.-C., et al.: Data glove embedded with 9-axis imu and force sensing sensors for evaluation of hand function. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4631–4634. IEEE (2015)

Bianchi, M., et al.: On the use of postural synergies to improve human hand pose reconstruction. In: 2012 IEEE Haptics Symposium (HAPTICS), pp. 91–98. IEEE (2012)

Bianchi, M., et al.: Synergy-based optimal design of hand pose sensing. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3929–3935, October 2012

10.

Bianchi, M., et al.: Synergy-based hand pose sensing: optimal glove design. Int. J. Robot. Res. 32(4), 407–424 (2013)CrossRef

11.

Bianchi, M., et al.: Exploiting hand kinematic synergies and wearable under-sensing for hand functional grasp recognition. In: 2014 EAI 4th International Conference on Wireless Mobile Communication and Healthcare (Mobihealth), pp. 168–171, November 2014

12.

Bianchi, M., et al.: A multi-modal sensing glove for human manual-interaction studies. Electronics 5(3), 42 (2016)CrossRef

13.

Santello, M., et al.: Postural hand synergies for tool use. J. Neurosci. 18(23), 10 105–10 115 (1998)

14.

Santello, M., et al.: Hand synergies: integration of robotics and neuroscience for understanding the control of biological and artificial hands. Phys. Life Rev. 17, 1–23 (2016)CrossRef

15.

Catalano, M.G., et al.: Adaptive synergies for the design and control of the Pisa/IIT softhand. Int. J. Robot. Res. 33(5), 768–782 (2014)CrossRef

16.

Matrone, G.C., et al.: Principal components analysis based control of a multi-dof underactuated prosthetic hand. J. Neuroeng. Rehabil. 7(1), 1 (2010)CrossRef

17.

Kennedy, J., et al.: Particle swarm optimization. In: International Conference on Neural Networks, vol. 4, no. 3, pp. 1942–1948. IEEE, January 1995

18.

Sun, X., et al.: Cascaded hand pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 824–832 (2015)

19.

Keskin, C., Kıraç, F., Kara, Y.E., Akarun, L.: Hand pose estimation and hand shape classification using multi-layered randomized decision forests. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 852–863. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_61 CrossRef

Titel: Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction
verfasst von: Simone Ciotti
Edoardo Battaglia
Iason Oikonomidis
Alexandros Makris
Aggeliki Tsoli
Antonio Bicchi
Antonis A. Argyros
Matteo Bianchi
Verlag: Springer International Publishing
Buch: Wireless Mobile Communication and Healthcare
Print ISBN: 978-3-319-58876-6

Electronic ISBN: 978-3-319-58877-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-58877-3_42

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