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Journal of NeuroEngineering and Rehabilitation

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Open Access 01-12-2019 | Research

A quantitative taxonomy of human hand grasps

Authors: Francesca Stival, Stefano Michieletto, Matteo Cognolato, Enrico Pagello, Henning Müller, Manfredo Atzori

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2019

Abstract

Background

A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified.

Methods

This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic.

Results

The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies.

Conclusions

The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields.

http://ninapro.hevs.ch/

url: http://www.cyberglovesystems.com/

The same mathematical expression is also known as Integrated EMG.

Cutkosky MR. On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans Robot Autom. 1989; 5(3):269–79.CrossRef

Atzori M, Müller H. Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees : A Scientific Research and Market Overview. Front Syst Neurosci. 2015; 9:162. https://doi.org/10.3389/fnsys.2015.00162.PubMedPubMedCentralCrossRef

Farina D, Jiang N, Rehbaum H, Holobar A, Graimann B, Dietl H, Aszmann OC. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng. 2014; 22(4):797–809. https://doi.org/10.1109/TNSRE.2014.2305111.PubMedCrossRef

Castellini C, Artemiadis P, Wininger M, Ajoudani A, Alimusaj M, Bicchi A, Caputo B, Craelius W, Dosen S, Englehart K, et al. Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography. Front Neurorobotics. 2014; 8:22.CrossRef

Santello M, Flanders M, Soechting JF. Postural hand synergies for tool use. J Neurosci. 1998; 18(23):10105–15. http://doi.org/citeulike-article-id:423192.PubMedCrossRef

Häger-Ross C, Schieber MH. Quantifying the independence of human finger movements: Comparisons of digits, hands, and movement frequencies. J Neurosci. 2000; 20(22):8542–50. https://doi.org/10.1523/JNEUROSCI.20-22-08542.2000. http://www.jneurosci.org/content/20/22/8542.full.pdf. Accessed 28 Jan 2019.PubMedCrossRefPubMedCentral

Eppner C, Deimel R, Álvarez-Ruiz J, Maertens M, Brock O. Exploitation of environmental constraints in human and robotic grasping. Int J Robot Res. 2015; 34(7):1021–38. https://doi.org/10.1177/0278364914559753.CrossRef

Stival F, Michieletto S, Pagello E. Subject Independent EMG Analysis by Using Low-Cost Hardware. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Piscataway: IEEE: 2018. p. 2766–2771. https://doi.org/10.1109/SMC.2018.00472.

Patel V, Thukral P, Burns MK, Florescu I, Chandramouli R, Vinjamuri R. Hand grasping synergies as biometrics. Front Bioeng Biotechnol. 2017; 5:26.PubMedPubMedCentralCrossRef

10.

Overduin SA, d’Avella A, Roh J, Bizzi E. Modulation of muscle synergy recruitment in primate grasping. J Neurosci. 2008; 28(4):880–92.PubMedCrossRefPubMedCentral

11.

Prevete R, Donnarumma F, d’Avella A, Pezzulo G. Evidence for sparse synergies in grasping actions. Sci Rep. 2018; 8(1):616.PubMedPubMedCentralCrossRef

12.

Napier JR. The prehensile movements of the human hand. Bone Joint J. 1956; 38(4):902–13.

13.

Landsmeer JMF. Power Grip and Precision Handling. Ann Rheum Dis. 1962; 21(2):164–70. https://doi.org/10.1136/ard.21.2.164.PubMedPubMedCentralCrossRef

14.

Kamakura N, Matsuo M, Ishii H, Mitsuboshi F, Miura Y. Patterns of static prehension in normal hands. Am J Occup Ther. 1980; 34(7):437–45. https://doi.org/10.5014/ajot.34.7.437.PubMedCrossRef

15.

Skerik SK, Weiss MW, Flatt AE. Functional evaluation of congenital hand anomalies. Am J Occup Ther Off Publ Am Occup Ther Assoc. 1971; 25(2):98–104.

16.

Cutkosky MR. On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans Robot Autom. 1989; 5(3):269–79. https://doi.org/10.1109/70.34763.CrossRef

17.

Feix T, Romero J, Schmiedmayer H-B, Dollar AM, Kragic D. The grasp taxonomy of human grasp types. IEEE Trans Hum-Mach Syst. 2016; 46(1):66–77.CrossRef

18.

Wolf K, Naumann A, Rohs M, Müller J. Taxonomy of microinteractions: Defining microgestures based on ergonomic and scenario-dependent requirements. In: Proceedings of the 13th IFIP TC 13 International Conference on Human-computer Interaction - Volume Part I. INTERACT’11. Berlin: Springer-Verlag: 2011. p. 559–75. http://dl.acm.org/citation.cfm?id=2042053.2042111.

19.

Bullock IM, Ma RR, Dollar AM. A hand-centric classification of human and robot dexterous manipulation. IEEE Trans Haptics. 2013; 6(2):129–44.PubMedCrossRef

20.

Cohen MF. An Introduction to Logic and Scientific Method. Estate House 144 Evesham Street, Redditch, Worcestershire B97 4HP: Read Books Ltd; 2013.

21.

Kessler GD, Hodges LF, Walker N. Evaluation of the cyberglove as a whole-hand input device. ACM Trans Comput-Hum Interact. 1995; 2(4):263–83.CrossRef

22.

Lee S-W, Zhang X. Development and evaluation of an optimization-based model for power-grip posture prediction. J Biomech. 2005; 38(8):1591–7.PubMedCrossRef

23.

Cerveri P, De Momi E, Lopomo N, Baud-Bovy G, Barros R, Ferrigno G. Finger kinematic modeling and real-time hand motion estimation. Ann Biomed Eng. 2007; 35(11):1989–2002.PubMedCrossRef

24.

Carpinella I, Mazzoleni P, Rabuffetti M, Thorsen R, Ferrarin M. Experimental protocol for the kinematic analysis of the hand: definition and repeatability. Gait Posture. 2006; 23(4):445–54.PubMedCrossRef

25.

Lee Y-H, Tsai C-Y. Taiwan sign language (tsl) recognition based on 3d data and neural networks. Expert Syst Appl. 2009; 36(2):1123–8.CrossRef

26.

Farina D, Merletti R, Enoka RM. The extraction of neural strategies from the surface EMG. J Appl Physiol. 2004; 96:1486–95. https://doi.org/10.1152/japplphysiol.01070.2003.PubMedCrossRef

27.

28.

De Luca CJ. The Use of Surface Electromyography in Biomechanics. J Appl Biomech. 1997; 13:135–63.CrossRef

29.

Atzori M, Gijsberts A, Castellini C, Caputo B, Hager A-GM, Elsig S, Giatsidis G, Bassetto F, Müller H. Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci Data. 2014; 1:140053.PubMedPubMedCentralCrossRef

30.

Atzori M, Gijsberts A, Castellini C, Caputo B, Hager A-GM, Simone E, Giatsidis G, Bassetto F, Müller H. Clinical parameter effect on the capability to control myoelectric robotic prosthetic hands. J Rehabil Res Dev. 2016; 53(3):345–58.PubMedCrossRef

31.

Gregori V, Gijsberts A, Caputo B. Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know. 2017. arXiv preprint arXiv:1702.08283.

32.

Kluge AG. A concern for evidence and a phylogenetic hypothesis of relationships among epicrates (boidae, serpentes). Syst Biol. 1989; 38(1):7–25.CrossRef

33.

Hinchliff CE, Smith SA, Allman JF, Burleigh JG, Chaudhary R, Coghill LM, Crandall KA, Deng J, Drew BT, Gazis R, Gude K, Hibbett DS, Katz LA, Laughinghouse HD, McTavish EJ, Midford PE, Owen CL, Ree RH, Rees JA, Soltis DE, Williams T, Cranston KA. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc Natl Acad Sci. 2015; 112(41):12764–9. https://doi.org/10.1073/pnas.1423041112. http://www.pnas.org/content/112/41/12764.full.pdf. Accessed 28 Jan 2019.PubMedCrossRef

34.

Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning. ICML ’09. New York: ACM: 2009. p. 609–16. https://doi.org/10.1145/1553374.1553453.

35.

Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell. 2013; 35(8):1915–29. https://doi.org/10.1109/TPAMI.2012.231.PubMedCrossRef

36.

Szalkai B, Grolmusz V. Seclaf: a webserver and deep neural network design tool for hierarchical biological sequence classification. Bioinformatics. 2018; 34(14):2487–9. https://doi.org/10.1093/bioinformatics/bty116.PubMedCrossRef

37.

Atzori M, Gijsberts A, Heynen S, Hager A-GM, Deriaz O, Van Der Smagt P, Castellini C, Caputo B, Müller H. Building the Ninapro database: A resource for the biorobotics community. In: 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). Piscataway: IEEE: 2012. p. 1258–65. https://doi.org/10.1109/BioRob.2012.6290287.

38.

Tsuji H, Ichinobe H, Ito K, Nagamachi M. Discrimination of forearm motions from emg signals by error back propagation typed neural network using entropy. IEEE Trans Soc Instrum Control Eng. 1993; 29(10):1213–20.

39.

Fukuda O, Tsuji T, Kaneko M, Otsuka A. A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Trans Robot Autom. 2003; 19(2):210–22.CrossRef

40.

Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011; 48(6):643. https://doi.org/10.1682/JRRD.2010.09.0177.PubMedCrossRef

41.

Hargrove LJ, Englehart K, Hudgins B. A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans Biomed Eng. 2007; 54(5):847–53. https://doi.org/10.1109/TBME.2006.889192.PubMedCrossRef

42.

Edwards SJ, Buckland DJ, McCoy-Powlen J. Developmental & Functional Hand Grasps. 6900 Grove Road | Thorofare, NJ 08086: SLACK Incorporated; 2002, p. 135.

43.

Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003; 50(7):848–54. https://doi.org/10.1109/TBME.2003.813539.PubMedCrossRef

44.

Hudgins B, Parker P, Scott RN. A New Strategy for Multifunction Myoelectric Control. IEEE Trans Biomed Eng. 1993; 40(1):82–94.PubMedCrossRef

45.

Gijsberts A, Atzori M, Castellini C, Müller H, Caputo B. Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification. IEEE Trans Neural Syst Rehabil Eng. 2014; 22(4):735–44. https://doi.org/10.1109/TNSRE.2014.2303394.PubMedCrossRef

46.

Mulgrew B, Grant P, Thompson J. Digital Signal Processing: Concepts and Applications. Crinan Street London N1 9XW: Macmillan Education UK; 1999.CrossRef

47.

Chan A, Green GC. Myoelectric control development toolbox. CMBES Proceedings. 2007; 30(1):M0100–1. https://proceedings.cmbes.ca/index.php/proceedings/article/view/129.

48.

49.

Kuzborskij I, Gijsberts A, Caputo B. On the challenge of classifying 52 hand movements from surface electromyography. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE: 2012. p. 4931–7. https://doi.org/10.1109/EMBC.2012.6347099.

50.

Lucas M-F, Gaufriau A, Pascual S, Doncarli C, Farina D. Multi-channel surface {EMG} classification using support vector machines and signal-based wavelet optimization. Biomed Signal Proc Control. 2008; 3(2):169–74. https://doi.org/10.1016/j.bspc.2007.09.002. Surface ElectromyographySurface Electromyography.CrossRef

51.

Oskoei MA, Hu H. Myoelectric control systems—a survey. Biomed Signal Proc Control. 2007; 2(4):275–94. https://doi.org/10.1016/j.bspc.2007.07.009.CrossRef

52.

Zecca M, Micera S, Carrozza MC, Dario P. Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal. Crit Rev Biomed Eng. 2002; 30(4-6):459–85. https://doi.org/10.1615/CritRevBiomedEng.v30.i456.80.PubMedCrossRef

53.

Zardoshti M, Wheeler BC, Badie K, Hashemi R. Evaluation of emg features for movement control of prostheses. In: Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ: 1993. p. 1141–2. https://doi.org/10.1109/IEMBS.1993.979061.

54.

Krzanowski W. Principles of Multivariate Analysis. Oxford Statistical Science Series, vol. 23. New York: Oxford University Press, Inc.; 2000.

55.

Mahalanobis PC. On the generalised distance in statistics. In: Proceedings National Institute of Science, India. Vol. 2, no. 1.1936. p. 49–55. http://ir.isical.ac.in/dspace/handle/1/1268.

56.

Pawlik M, Augsten N. Tree edit distance: Robust and memory-efficient. Inf Syst. 2016; 56:157–173. https://doi.org/10.1016/j.is.2015.08.004. http://www.sciencedirect.com/science/article/pii/S0306437915001611.CrossRef

57.

Pawlik M, Augsten N. Efficient computation of the tree edit distance. ACM Trans Database Syst. 2015; 40(1):3–1340.CrossRef

58.

Stival F, Michieletto S, Pagello E, Müller H, Atzori M. Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data. In: Workshop Proceedings of the 15th International Conference on Autonomous Systems IAS-15, Workshop on Learning Applications for Intelligent Autonomous Robots (LAIAR-2018). Karlsruhe: Strand: 2018. ISBN: 978-3-00-059946-0.

59.

Whidden C, Zeh N, Beiko RG. Supertrees based on the subtree prune-and-regraft distance. Syst Biol. 2014; 63(4):566–581. https://doi.org/10.1093/sysbio/syu023.PubMedPubMedCentralCrossRef

60.

Eccarius P, Bour R, Scheidt RA. Dataglove measurement of joint angles in sign language handshapes. Sign Lang Linguist. 2012; 15(1):39–72. https://doi.org/10.1075/sll.15.1.03ecc.PubMedPubMedCentralCrossRef

61.

Gracia-Ibáñez V, Vergara M, Buffi JH, Murray WM, Sancho-Bru JL. Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes. Comput Methods Biomech Biomed Eng. 2017; 20(6):587–97. https://doi.org/10.1080/10255842.2016.1265950.CrossRef

62.

Stival F, Moro M, Pagello E. A first approach to a taxonomy-based classification framework for hand grasps. In: Workshop Proceedings of the 15th International Conference on Autonomous Systems IAS-15, Workshop on Learning Applications for Intelligent Autonomous Robots (LAIAR-2018). Karlsruhe: Strand: 2018. ISBN: 978-3-00-059946-0.

Title: A quantitative taxonomy of human hand grasps
Authors: Francesca Stival
Stefano Michieletto
Matteo Cognolato
Enrico Pagello
Henning Müller
Manfredo Atzori
Publication date: 01-12-2019
Publisher: BioMed Central
Published in: Journal of NeuroEngineering and Rehabilitation / Issue 1/2019
Electronic ISSN: 1743-0003
DOI: https://doi.org/10.1186/s12984-019-0488-x

At a glance: The STEP trials

Springer Medicine

A quantitative taxonomy of human hand grasps

Abstract

Background

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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