Abstract
The human hand is our preeminent and most versatile tool to explore and modify the external environment. It represents both the cognitive organ of the sense of touch and the most important end effector in object manipulation and grasping. Our brain can cope efficiently with the high degree of complexity of the hand, which arises from the huge amount of actuators and sensors. This allows us to perform a large number of daily life tasks, from the simple ones, such as determining the ripeness of a fruit or drive a car, to the more complex ones, as for example performing surgical procedures, playing an instrument or painting. Not surprisingly, an intensive research effort has been devoted to (i) understand the neuroanatomical and physiological mechanisms underpinning the sensorimotor control of human hands and (ii) to attempt to reproduce such mechanisms in artificial robotic systems.
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Ajoudani A, Tsagarakis NG, Bicchi A (2012) Tele-impedance: teleoperation with impedance regulation using a body-machine interface. Int J Robot Res p 0278364912464668
Bianchi M (2012) On the role of haptic synergies in modelling the sense of touch and in designing artificial haptic systems. PhD thesis, Pisa, Italy
Bianchi M, Salaris P, Bicchi A (2013) Synergy-based hand pose sensing: optimal glove design. Int J Robot Res 32(4):407–424
Bicchi A, Gabiccini M, Santello M (2011) Modelling natural and artificial hands with sinergie. Phil Trans R Soc B 366:3153–3161
Brown C, Asada H (2007) Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal component analysis. In: IEEE-RAS international conference on intelligent robots and systems, pp 2877–2882
Castellini C, Fiorilla AE, Sandini G (2009) Multi-subject/daily-life activity emg-based control of mechanical hands. J Neuroeng Rehabil 6(1):41
Catalano MG, Grioli G, Farnioli E, Serio A, Piazza C, Bicchi A (2014) Adaptive synergies for the design and control of the pisa/iit softhand. Int J Robot Res 33:768–782
Ciocarlie MT, Goldfeder C, Allen PK (2007) Dimensionality reduction for hand-independent dexterous robotic grasping. In: IEEE/RSJ international conference on intelligent robots and systems, pp 3270–3275
Ehrsson HH, Kuhtz-Buschbeck JP, Forssberg H (2002) Brain regions controlling nonsynergistic versus synergistic movement of the digits: a functional magnetic resonance imaging study. J Neurosci 22(12):5074–5080
Filippidis IF, Kyriakopoulos KJ, Artemiadis PK (2012) Navigation functions learning from experiments: application to anthropomorphic grasping. In: 2012 IEEE international conference on robotics and automation (ICRA), pp 570–575. IEEE
Gabiccini M, Farnioli E, Bicchi A (2013) Grasp analysis tools for synergistic underactuated robotic hands. Int J Robot Res p 0278364913504473
Gioioso G, Salvietti G, Malvezzi M, Prattichizzo D (2013) An object-based approach to map human hand synergies onto robotic hands with dissimilar kinematics. Robotics p 97
Moscatelli A, Bianchi M, Serio A, Al Atassi O, Fani S, Terekhov A, Hayward V, Ernst M, Bicchi A (2014) A change in the fingertip contact area induces an illusory displacement of the finger. In: Haptics: neuroscience, devices, modeling, and applications, pp 72–79. Springer
Naceri A, Moscatelli A, Santello M, Ernst M (2014) Coordination of multi-digit positions and forces during unconstrained grasping in response to object perturbations. In: Haptics symposium (HAPTICS), 2014 IEEE, pp 35–40
Santello M, Baud-Bovy G, Jorntell H (2013) Neural bases of hand synergies. Front Comput Neurosci 7
Schieber MH, Santello M (2004) Hand function: peripheral and central constraints on performance. J Appl Physiol 96(6):2293–2300
Soechting JF, Flanders M (1997) Hand synergies during reach-tograsp. J Comput Neurosci 4:29–46
Spanne A, Jorntell H (2013) Processing of multi-dimensional sensori- motor information in the spinal and cerebellar neuronal circuitry: a new hypothesis. PLoS Comput Biol 9(3):e1002979
Taylor AM, Enoka RM (2004) Quantification of the factors that influence discharge correlation in model motor neurons. J Neurophysiol 91(2):796–814
Valero-Cuevas FJ (2000) Predictive modulation of muscle coordination pattern magnitude scales fingertip force magnitude over the voluntary range. J Neurophysiol 83(3):1469–1479
van Polanen V, Tiest WMB, Kappers AM (2014) Target contact and exploration strategies in haptic search. Sci Rep 4
Zatsiorsky VM, Li Z-M, Latash ML (2000) Enslaving effects in multi-finger force production. Exp Brain Res 131(2):187–195
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Bianchi, M., Moscatelli, A. (2016). Introduction. In: Bianchi, M., Moscatelli, A. (eds) Human and Robot Hands. Springer Series on Touch and Haptic Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-26706-7_1
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DOI: https://doi.org/10.1007/978-3-319-26706-7_1
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