Mechatronic designs for a robotic hand to explore human body experience and sensory-motor skills: a Delphi study

References

• Beckerle P, Salvietti G, Unal R, et al. A human-robot interaction perspective on assistive and rehabilitation robotics. Front Neurorobot. 2017;11:24. doi: 10.3389/fnbot.2017.00024
   PubMed Web of Science ®Google Scholar
• Mayer A, Kudar K, Bretz K, et al. Body schema and body awareness of amputees. Prosthet Orthot Int. 2008;32(3):363–382. doi: 10.1080/03093640802024971
   PubMed Web of Science ®Google Scholar
• Gallagher S, Cole J. Body schema and body image in a deafferented subject. J Mind Behav. 1995;16:369–390.
   Web of Science ®Google Scholar
• Christ O, Reiner M. Perspectives and possible applications of the rubber hand and virtual hand illusion in non-invasive rehabilitation: technological improvements and their consequences. Neurosci Biobehav Rev. 2014;44:33–44. doi: 10.1016/j.neubiorev.2014.02.013
   PubMed Web of Science ®Google Scholar
• Botvinick M, Cohen J. Rubber hands 'feel' touch that eyes see. Nature. 1998;391:756. doi: 10.1038/35784
   PubMed Web of Science ®Google Scholar
• Tsakiris M, Carpenter L, James D, et al. Hands only illusion: multisensory integration elicits sense of ownership for body parts but not for non-corporeal objects. Exp Brain Res. 2010;204(3):343–352. doi: 10.1007/s00221-009-2039-3
   PubMed Web of Science ®Google Scholar
• Maister L, Sebanz N, Knoblich G, et al. Experiencing ownership over a dark-skinned body reduces implicit racial bias. Cognition. 2013;128(2):170–178. doi: 10.1016/j.cognition.2013.04.002
   PubMed Web of Science ®Google Scholar
• Caspar EA, de Beir A, Magalhães Da Saldanha da Gama PA, et al. New frontiers in the rubber hand experiment: when a robotic hand becomes one's own. Behav Res Methods. 2015;47(3):744–755. doi: 10.3758/s13428-014-0498-3
   PubMed Web of Science ®Google Scholar
• Romano R, Caffa E, Hernandez-Arieta A, et al. The robot hand illusion: inducing proprioceptive drift through visuo-motor congruency. Neuropsychologia. 2015;70:414–420. doi: 10.1016/j.neuropsychologia.2014.10.033
   PubMed Web of Science ®Google Scholar
• Beckerle P, De Beir A, Schürmann T, et al. Human body schema exploration: analyzing design requirements of robotic hand and leg illusions. IEEE International Symposium on Robot and Human Interactive Communication; 2016; New York, NY, USA.
   Google Scholar
• Christ O, Beckerle P, Preller J, et al. The rubber hand illusion: maintaining factors and a new perspective in rehabilitation and biomedical engineering?. Biomed Eng. 2012;57(S1):1098–1101.
   Google Scholar
• Santello M, Baud-Bovy G, Jörntell H. Neural bases of hand synergies. Front Comput Neurosci. 2013;7:23. doi: 10.3389/fncom.2013.00023
   PubMed Web of Science ®Google Scholar
• Brown CY, Asada HH. Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis. IEEE/RSJ International Conference on Intelligent Robots and Systems; 2007
   Google Scholar
• Ciocarlie MT, Allen PK. Hand posture subspaces for dexterous robotic grasping. Int J Robot Res. 2009;28:851–867. doi: 10.1177/0278364909105606
   Web of Science ®Google Scholar
• Ficuciello F, Palli G, Melchiorri C, et al. Experimental evaluation of postural synergies during reach to grasp with the ub hand iv. In: IEEE/RSJ International Conference on Intelligent Robots and Systems; 2011
   Google Scholar
• Liarokapis M, Artemiadis P, Bechlioulis CP, et al. Directions, methods and metrics for mapping human to robot motion with functional anthropomorphism: a review. National Technical University of Athens; 2013. (Report no). Available from: https://minasliarokapis.com/2013_TR_DirectionsMethodsMetricsMappingHumanRobotMotion.pdf.
   Google Scholar
• Santello M, Bianchi M, Gabiccini M, et al. Hand synergies: integration of robotics and neuroscience for understanding the control of biological and artificial hands. Phys Life Rev. 2016;17:1–23. doi: 10.1016/j.plrev.2016.02.001
   PubMed Web of Science ®Google Scholar
• Catalano MG, Grioli G, Farnioli E, et al. Adaptive synergies for the design and control of the pisa/iit softhand. Int J Rob Res. 2014;33(5):768–782. doi: 10.1177/0278364913518998
   Web of Science ®Google Scholar
• Santello M, Flanders M, Soechting JF. Postural hand synergies for tool use. J Neurosci. 1998;18(23):10105–10115. doi: 10.1523/JNEUROSCI.18-23-10105.1998
   PubMed Web of Science ®Google Scholar
• Godfrey SB, Bianchi M, Zhao K, et al. Converging clinical and engineering research on neurorehabilitation ii. Springer; 2017. Chapter The softhand pro: translation from robotic hand to prosthetic prototype; p. 469–473.
   Google Scholar
• Dalley SA, Wiste TE, Withrow TJ, et al. Design of a multifunctional anthropomorphic prosthetic hand with extrinsic actuation. IEEE ASME Trans Mechatron. 2009;14(6):699–706. doi: 10.1109/TMECH.2009.2033113
   Web of Science ®Google Scholar
• Micera S, Carrozza MC, Beccai L, et al. Hybrid bionic systems for the replacement of hand function. Proc IEEE. 2006;94(9):1752–1762. doi: 10.1109/JPROC.2006.881294
   Web of Science ®Google Scholar
• Gioioso G, Salvietti G, Malvezzi M, et al. Mapping synergies from human to robotic hands with dissimilar kinematics: an approach in the object domain. IEEE Trans Robot. 2013;29(4):825–837. doi: 10.1109/TRO.2013.2252251
   Web of Science ®Google Scholar
• Salvietti G, Wimboeck T, Prattichizzo D. A static intrinsically passive controller to enhance grasp stability of object-based mapping between human and robotic hands. IEEE/RSJ International Conference of Intelligent Robots and Systems; 2013; Tokyo, Japan.
   Google Scholar
• Padilla MA, Pabon S, Frisoli A, et al. Hand and arm ownership illusion through virtual reality physical interaction and vibrotactile stimulations. In: EuroHaptics 2010; 2010. p. 194–199
   Google Scholar
• Kalckert A, Ehrsson HH. The moving rubber hand illusion revisited: comparing movements and visuotactile stimulation to induce illusory ownership. Conscious Cogn. 2014;26:117–132. doi: 10.1016/j.concog.2014.02.003
   PubMed Web of Science ®Google Scholar
• Ma K, Hommel B. Body-ownership for actively operated non-corporeal objects. Conscious Cogn. 2015;36:75–86. doi: 10.1016/j.concog.2015.06.003
   PubMed Web of Science ®Google Scholar
• Hara M, Nabae H, Yamamoto A, et al. A novel rubber hand illusion paradigm allowing active self-touch with variable force feedback controlled by a haptic device. IEEE Trans Hum Mach Syst. 2016;46(1):78–87. doi: 10.1109/THMS.2015.2487499
   Web of Science ®Google Scholar
• Choi W, Li L, Satoh S, et al. Multisensory integration in the virtual hand illusion with active movement. BioMed Research International. 2016;2016:8163098.
   Google Scholar
• Pahl G, Beitz W, Feldhusen J, et al. Engineering design – a systematic approach. London: Springer; 2007.
   Google Scholar
• van der Linde H, Hofstad CJ, van Limbeek J, et al. Use of the Delphi technique for developing national clinical guidelines for prescription of lower-limb prostheses. J Reha Res Dev. 2005;42(5):693–704. doi: 10.1682/JRRD.2003.11.0172
   PubMedGoogle Scholar
• Schaffalitzky EM, Gallagher P, MacLachlan M, et al. Developing consensus on important factors associated with lower limb prosthetic prescription and use. Disabil Rehab. 2012;34(24):2085–2094. doi: 10.3109/09638288.2012.671885
   PubMed Web of Science ®Google Scholar
• Hernandez Arieta A, Katoh R, Yokoi H, et al. Development of a multi-dof electromyography prosthetic system using the adaptive joint mechanism. Appl Bionics Biomech. 2006;3(2):101–111. doi: 10.1155/2006/741851
   Google Scholar
• von der Gracht HA. Consensus measurement in Delphi studies: review and implications for future quality assurance. Technol Forecast Soc Change. 2012;79(8):1525–1536. doi: 10.1016/j.techfore.2012.04.013
   Web of Science ®Google Scholar
• Diamond IR, Grant RC, Feldman BM, et al. Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies. J Clin Epidemiol. 2014;67(4):401–409. doi: 10.1016/j.jclinepi.2013.12.002
   PubMed Web of Science ®Google Scholar
• Holey EA, Feeley JL, Dixon J, et al. An exploration of the use of simple statistics to measure consensus and stability in Delphi studies. BMC Med Res Methodol. 2007;7:52. doi: 10.1186/1471-2288-7-52
   PubMed Web of Science ®Google Scholar
• Sturman DJ. Whole-hand input [dissertation]. Massachusetts Institute of Technology; 1992
   Google Scholar
• Gabiccini M, Stillfried G, Marino H, et al. A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis. IEEE/RSJ International Conference on Intelligent Robots and Systems; 2013; Tokyo, Japan.
   Google Scholar
• Cerulo I, Ficuciello F, Lippiello V, et al. Teleoperation of the Schunk s5fh under-actuated anthropomorphic hand using human hand motion tracking. Rob Auton Syst. 2017;89:75–84. doi: 10.1016/j.robot.2016.12.004
   Web of Science ®Google Scholar
• Riek LD, Rabinowitch TC, Chakrabarti B, et al. How anthropomorphism affects empathy toward robots. ACM/IEEE International Conference on Human-Robot Interaction; 2009; La Jolla, CA, USA.
   Google Scholar
• Kim YJ, Lee Y, Kim J, et al. Roboray hand : a highly backdrivable robotic hand with sensorless contact force measurements. IEEE International Conference on Robotics and Automation; 2014; Hong Kong, China.
   Google Scholar
• Della Santina C, Piazza C, Gasparri GM, et al. An open platform to fast-prototyping articulated soft robots. IEEE Robotics Autom Mag. 2017;24(1):48–56. doi: 10.1109/MRA.2016.2636366
   Web of Science ®Google Scholar