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Bensmaia SJ, Tyler DJ, Micera S. Restoration of sensory information via bionic hands. Nat Biomed Eng 2023; 7:443-455. [PMID: 33230305 PMCID: PMC10233657 DOI: 10.1038/s41551-020-00630-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 09/13/2020] [Indexed: 12/19/2022]
Abstract
Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.
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Affiliation(s)
- Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.
| | - Dustin J Tyler
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Federale de Lausanne, Lausanne, Switzerland.
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2
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Pestell N, Griffith T, Lepora NF. Artificial SA-I and RA-I afferents for tactile sensing of ridges and gratings. J R Soc Interface 2022; 19:20210822. [PMID: 35382575 PMCID: PMC8984303 DOI: 10.1098/rsif.2021.0822] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
For robot touch to reach the capabilities of human touch, artificial tactile sensors may require transduction principles like those of natural tactile afferents. Here we propose that a biomimetic tactile sensor (the TacTip) could provide suitable artificial analogues of the tactile skin dynamics, afferent responses and population encoding. Our three-dimensionally printed sensor skin is based on the physiology of the dermal-epidermal interface with an underlying mesh of biomimetic intermediate ridges and dermal papillae, comprising inner pins tipped with markers. Slowly adapting SA-I activity is modelled by marker displacements and rapidly adapting RA-I activity by marker speeds. We test the biological plausibility of these artificial population codes with three classic experiments used for natural touch: (1a) responses to normal pressure to test adaptation of single afferents and spatial modulation across the population; (1b) responses to bars, edges and gratings to compare with measurements from monkey primary afferents; and (2) discrimination of grating orientation to compare with human perceptual performance. Our results show a match between artificial and natural touch at single afferent, population and perceptual levels. As expected, natural skin is more sensitive, which raises a challenge to fabricate a biomimetic fingertip that demonstrates human sensitivity using the transduction principles of human touch.
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Affiliation(s)
- Nicholas Pestell
- Department of Engineering Mathematics and Bristol Robotics Laboratory, University of Bristol, Bristol BS8 1QU, UK
| | - Thom Griffith
- Department of Engineering Mathematics and Bristol Robotics Laboratory, University of Bristol, Bristol BS8 1QU, UK
| | - Nathan F Lepora
- Department of Engineering Mathematics and Bristol Robotics Laboratory, University of Bristol, Bristol BS8 1QU, UK
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Sobinov AR, Bensmaia SJ. The neural mechanisms of manual dexterity. Nat Rev Neurosci 2021; 22:741-757. [PMID: 34711956 DOI: 10.1038/s41583-021-00528-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2021] [Indexed: 01/22/2023]
Abstract
The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.
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Affiliation(s)
- Anton R Sobinov
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.,Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA. .,Neuroscience Institute, University of Chicago, Chicago, IL, USA. .,Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
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Komeno N, Matsubara T. Tactile Perception Based on Injected Vibration in Soft Sensor. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3075664] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Senkow TL, Theis ND, Quindlen-Hotek JC, Barocas VH. Computational and Psychophysical Experiments on the Pacinian Corpuscle's Ability to Discriminate Complex Stimuli. IEEE TRANSACTIONS ON HAPTICS 2019; 12:635-644. [PMID: 30932849 DOI: 10.1109/toh.2019.2903500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Recognizing and discriminating vibrotactile stimuli is an essential function of the Pacinian corpuscle. This function has been studied at length in both a computational and an experimental setting, but the two approaches have rarely been compared, especially when the computational model has a high level of structural detail. In this paper, we explored whether the predictions of a multiscale, multiphysical computational model of the Pacinian corpuscle can predict the outcome of a corresponding psychophysical experiment. The discrimination test involved either two simple stimuli with frequency in the 160-500 Hz range, or two complex stimuli formed by combining the waveforms for a 100-Hz stimulus with a second stimulus in the 160-500 Hz range. The subjects' ability to distinguish between the simple stimuli increased as the frequency increased, a result consistent with the model predictions for the same stimuli. The model also predicted correctly that subjects would find the complex stimuli more difficult to distinguish than the simple ones and also that the discriminability of the complex stimuli would show no trend with frequency difference.
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Seminara L, Gastaldo P, Watt SJ, Valyear KF, Zuher F, Mastrogiovanni F. Active Haptic Perception in Robots: A Review. Front Neurorobot 2019; 13:53. [PMID: 31379549 PMCID: PMC6651744 DOI: 10.3389/fnbot.2019.00053] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Accepted: 07/01/2019] [Indexed: 11/13/2022] Open
Abstract
In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today's new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data.
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Affiliation(s)
- Lucia Seminara
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, University of Genoa, Genoa, Italy
| | - Paolo Gastaldo
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, University of Genoa, Genoa, Italy
| | - Simon J. Watt
- School of Psychology, Bangor University, Bangor, United Kingdom
| | | | - Fernando Zuher
- Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil
| | - Fulvio Mastrogiovanni
- Department of Computer Science, Bioengineering, Robotics, and Systems Engineering, University of Genoa, Genoa, Italy
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Pestell N, Lloyd J, Rossiter J, Lepora NF. Dual-Modal Tactile Perception and Exploration. IEEE Robot Autom Lett 2018. [DOI: 10.1109/lra.2018.2794609] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Ward-Cherrier B, Pestell N, Cramphorn L, Winstone B, Giannaccini ME, Rossiter J, Lepora NF. The TacTip Family: Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies. Soft Robot 2018; 5:216-227. [PMID: 29297773 PMCID: PMC5905869 DOI: 10.1089/soro.2017.0052] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Tactile sensing is an essential component in human-robot interaction and object manipulation. Soft sensors allow for safe interaction and improved gripping performance. Here we present the TacTip family of sensors: a range of soft optical tactile sensors with various morphologies fabricated through dual-material 3D printing. All of these sensors are inspired by the same biomimetic design principle: transducing deformation of the sensing surface via movement of pins analogous to the function of intermediate ridges within the human fingertip. The performance of the TacTip, TacTip-GR2, TacTip-M2, and TacCylinder sensors is here evaluated and shown to attain submillimeter accuracy on a rolling cylinder task, representing greater than 10-fold super-resolved acuity. A version of the TacTip sensor has also been open-sourced, enabling other laboratories to adopt it as a platform for tactile sensing and manipulation research. These sensors are suitable for real-world applications in tactile perception, exploration, and manipulation, and will enable further research and innovation in the field of soft tactile sensing.
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Affiliation(s)
- Benjamin Ward-Cherrier
- 1 Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom .,2 Bristol Robotics Laboratory , Bristol, United Kingdom
| | - Nicholas Pestell
- 1 Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom .,2 Bristol Robotics Laboratory , Bristol, United Kingdom
| | | | | | - Maria Elena Giannaccini
- 1 Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom .,2 Bristol Robotics Laboratory , Bristol, United Kingdom
| | - Jonathan Rossiter
- 1 Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom .,2 Bristol Robotics Laboratory , Bristol, United Kingdom
| | - Nathan F Lepora
- 1 Department of Engineering Mathematics, University of Bristol , Bristol, United Kingdom .,2 Bristol Robotics Laboratory , Bristol, United Kingdom
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