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      Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics

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          Abstract

          The feeling of embodiment, i.e., experiencing the body as belonging to oneself and being able to integrate objects into one's bodily self-representation, is a key aspect of human self-consciousness and has been shown to importantly shape human cognition. An extension of such feelings toward robots has been argued as being crucial for assistive technologies aiming at restoring, extending, or simulating sensorimotor functions. Empirical and theoretical work illustrates the importance of sensory feedback for the feeling of embodiment and also immersion; we focus on the the perceptual level of touch and the role of tactile feedback in various assistive robotic devices. We critically review how different facets of tactile perception in humans, i.e., affective, social, and self-touch, might influence embodiment. This is particularly important as current assistive robotic devices – such as prostheses, orthoses, exoskeletons, and devices for teleoperation–often limit touch low-density and spatially constrained haptic feedback, i.e., the mere touch sensation linked to an action. Here, we analyze, discuss, and propose how and to what degree tactile feedback might increase the embodiment of certain robotic devices, e.g., prostheses, and the feeling of immersion in human-robot interaction, e.g., in teleoperation. Based on recent findings from cognitive psychology on interactive processes between touch and embodiment, we discuss technical solutions for specific applications, which might be used to enhance embodiment, and facilitate the study of how embodiment might alter human-robot interactions. We postulate that high-density and large surface sensing and stimulation are required to foster embodiment of such assistive devices.

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          Most cited references74

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          Stretchable silicon nanoribbon electronics for skin prosthesis.

          Sensory receptors in human skin transmit a wealth of tactile and thermal signals from external environments to the brain. Despite advances in our understanding of mechano- and thermosensation, replication of these unique sensory characteristics in artificial skin and prosthetics remains challenging. Recent efforts to develop smart prosthetics, which exploit rigid and/or semi-flexible pressure, strain and temperature sensors, provide promising routes for sensor-laden bionic systems, but with limited stretchability, detection range and spatio-temporal resolution. Here we demonstrate smart prosthetic skin instrumented with ultrathin, single crystalline silicon nanoribbon strain, pressure and temperature sensor arrays as well as associated humidity sensors, electroresistive heaters and stretchable multi-electrode arrays for nerve stimulation. This collection of stretchable sensors and actuators facilitate highly localized mechanical and thermal skin-like perception in response to external stimuli, thus providing unique opportunities for emerging classes of prostheses and peripheral nervous system interface technologies.
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            Tactile Sensing—From Humans to Humanoids

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              Restoring natural sensory feedback in real-time bidirectional hand prostheses.

              Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user's intentions and the delivery of nearly "natural" sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and "life-like" quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.
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                Author and article information

                Contributors
                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                11 December 2018
                2018
                : 12
                : 84
                Affiliations
                [1] 1Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund , Dortmund, Germany
                [2] 2Institute for Mechatronic Systems, Mechanical Engineering, Technische Universität Darmstadt , Darmstadt, Germany
                [3] 3Neuroinformatics Group, Center of Excellence Cognitive Interaction Technology, Bielefeld University , Bielefeld, Germany
                [4] 4German Research Center for Artificial Intelligence, Robotics Innovation Center , Bremen, Germany
                [5] 5Robotics Group, University of Bremen , Bremen, Germany
                [6] 6Department of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University , Mannheim, Germany
                [7] 7Department of Health Science and Technology, Faculty of Medicine, Center for Sensory-Motor Interaction, Aalborg University , Aalborg, Denmark
                [8] 8School of Applied Psychology, Institute Humans in Complex Systems, University of Applied Sciences and Arts Northwestern Switzerland , Olten, Switzerland
                [9] 9Delft Haptics Lab, Department of Cognitive Robotics, Faculty 3mE, Delft University of Technology , Delft, Netherlands
                [10] 10DLR German Aerospace Center, Institute of Robotics and Mechatronics , Oberpfaffenhofen, Germany
                [11] 11Cognitive Neuropsychology, Department of Psychology, University of Zurich , Zurich, Switzerland
                Author notes

                Edited by: Sung-Phil Kim, Ulsan National Institute of Science and Technology, South Korea

                Reviewed by: Solaiman Shokur, Alberto Santos Dumont Association for Research Support, Brazil; Stéphane Lallée, Facebook, United States

                *Correspondence: Philipp Beckerle philipp.beckerle@ 123456tu-dortmund.de
                Article
                10.3389/fnbot.2018.00084
                6297195
                30618706
                c9e9f9c6-8ada-4c93-9ce3-ee87133a49d5
                Copyright © 2018 Beckerle, Kõiva, Kirchner, Bekrater-Bodmann, Dosen, Christ, Abbink, Castellini and Lenggenhager.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 September 2018
                : 26 November 2018
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 81, Pages: 7, Words: 6455
                Funding
                Funded by: Deutsche Forschungsgemeinschaft 10.13039/501100001659
                Award ID: BE 5729/3
                Award ID: BE 5729/11
                Award ID: CA 1389/1
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10.13039/501100001711
                Award ID: 170511
                Funded by: Danmarks Frie Forskningsfond 10.13039/501100011958
                Award ID: 8022-00243A
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10.13039/501100001711
                Award ID: 8022-00243A
                Categories
                Neuroscience
                Perspective

                Robotics
                embodiment,affective touch,social touch,self-touch,human-machine interfaces,tactile feedback,assistive robotics

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