A quantitative taxonomy of human hand grasps

BackgroundA 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.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.

[1]  Manfredo Atzori,et al.  Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees: A Scientific Research and Market Overview , 2015, Front. Syst. Neurosci..

[2]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[3]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

[4]  Roberto Merletti,et al.  The extraction of neural strategies from the surface EMG. , 2004, Journal of applied physiology.

[5]  Xudong Zhang,et al.  Development and evaluation of an optimization-based model for power-grip posture prediction. , 2005, Journal of biomechanics.

[6]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[7]  G. Ferrigno,et al.  Finger Kinematic Modeling and Real-Time Hand Motion Estimation , 2007, Annals of Biomedical Engineering.

[8]  Enrico Pagello,et al.  Subject Independent EMG Analysis by Using Low-Cost Hardware , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[9]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[10]  N. Zeh,et al.  Supertrees Based on the Subtree Prune-and-Regraft Distance , 2014, Systematic biology.

[11]  Toshio Tsuji,et al.  Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy , 1993 .

[12]  J. G. Burleigh,et al.  Synthesis of phylogeny and taxonomy into a comprehensive tree of life , 2014, Proceedings of the National Academy of Sciences.

[13]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[14]  Vince Grolmusz,et al.  SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification , 2018, Bioinform..

[15]  A. Kluge.  A Concern for Evidence and a Phylogenetic Hypothesis of Relationships among Epicrates (Boidae, Serpentes) , 1989 .

[16]  Robert A Scheidt,et al.  Dataglove measurement of joint angles in sign language handshapes. , 2012, Sign language and linguistics.

[17]  J. Napier.  The prehensile movements of the human hand. , 1956, The Journal of bone and joint surgery. British volume.

[18]  Levi J. Hargrove,et al.  A Comparison of Surface and Intramuscular Myoelectric Signal Classification , 2007, IEEE Transactions on Biomedical Engineering.

[19]  M. Ferrarin,et al.  Experimental protocol for the kinematic analysis of the hand: definition and repeatability. , 2006, Gait & posture.

[20]  Nikolaus Augsten,et al.  Tree edit distance: Robust and memory-efficient , 2016, Inf. Syst..

[21]  Aaron M. Dollar,et al.  A Hand-Centric Classification of Human and Robot Dexterous Manipulation , 2013, IEEE Transactions on Haptics.

[22]  Manfredo Atzori,et al.  Building the Ninapro database: A resource for the biorobotics community , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[23]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[25]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[26]  Danica Kragic,et al.  The GRASP Taxonomy of Human Grasp Types , 2016, IEEE Transactions on Human-Machine Systems.

[27]  J. Landsmeer.  Power Grip and Precision Handling , 1962, Annals of the rheumatic diseases.

[28]  A. M. Hager,et al.  Effect of clinical parameters on the control of myoelectric robotic prosthetic hands. , 2016, Journal of rehabilitation research and development.

[29]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[30]  John Thompson,et al.  Digital Signal Processing: Concepts and Applications , 1999 .

[31]  John K. Ryan,et al.  An Introduction to Logic and Scientific Method , 1935 .

[32]  M H Schieber,et al.  Quantifying the Independence of Human Finger Movements: Comparisons of Digits, Hands, and Movement Frequencies , 2000, The Journal of Neuroscience.

[33]  Ernest Nagel,et al.  An Introduction to Logic and Scientific Method , 1934, Nature.

[34]  Barbara Caputo,et al.  Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[35]  Yung-Hui Lee,et al.  Taiwan sign language (TSL) recognition based on 3D data and neural networks , 2009, Expert Syst. Appl..

[36]  N. Kamakura,et al.  Patterns of static prehension in normal hands. , 1980, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[37]  Margarita Vergara,et al.  Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes , 2017, Computer methods in biomechanics and biomedical engineering.

[38]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[39]  Oliver Brock,et al.  Exploitation of environmental constraints in human and robotic grasping , 2015, Int. J. Robotics Res..

[40]  Neff Walker,et al.  Evaluation of the CyberGlove as a whole-hand input device , 1995, TCHI.

[41]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[42]  Simon A. Overduin,et al.  Modulation of Muscle Synergy Recruitment in Primate Grasping , 2008, The Journal of Neuroscience.

[43]  S. Edwards,et al.  Developmental and functional hand grasps , 2002 .

[44]  Giovanni Pezzulo,et al.  Evidence for sparse synergies in grasping actions , 2018, Scientific Reports.

[45]  A. Flatt,et al.  Functional evaluation of congenital hand anomalies. , 1971, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[46]  Ilja Kuzborskij,et al.  On the challenge of classifying 52 hand movements from surface electromyography , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[47]  Nikolaus Augsten,et al.  Efficient Computation of the Tree Edit Distance , 2015, TODS.

[48]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[49]  Manfredo Atzori,et al.  Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  Bruce C. Wheeler,et al.  Evaluation of emg features for movement control of prostheses , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[51]  Michael Rohs,et al.  A Taxonomy of Microinteractions: Defining Microgestures Based on Ergonomic and Scenario-Dependent Requirements , 2011, INTERACT.

[52]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[53]  Adrian D. C. Chan,et al.  Myoelectric Control Development Toolbox , 2007 .

[54]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[55]  Panagiotis K. Artemiadis,et al.  Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography , 2014, Front. Neurorobot..

[56]  Erik Scheme,et al.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.

[57]  P. Dario,et al.  Control of multifunctional prosthetic hands by processing the electromyographic signal. , 2002, Critical reviews in biomedical engineering.

[58]  Rajarathnam Chandramouli,et al.  Hand Grasping Synergies As Biometrics , 2017, Front. Bioeng. Biotechnol..