Roughness Encoding in Human and Biomimetic Artificial Touch: Spatiotemporal Frequency Modulation and Structural Anisotropy of Fingerprints

Oddo, Calogero Maria; Beccai, Lucia; Wessberg, Johan; Wasling, Helena Backlund; Mattioli, Fábio; Carrozza, Maria Chiara

doi:10.3390/s110605596

Cited by 45 publications

(35 citation statements)

References 45 publications

(63 reference statements)

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“…In general, however, scanning speeds commonly falls in the range from 80 to 100 mm/s (Gamzu and Ahissar 2001;Smith et al 2002a,b;Tanaka et al 2014), matching reasonably well the average speeds averaged across tasks, textures, and subjects in the present study. Determining the range of scanning speeds during natural texture exploration has implications for studies on the neural basis of texture, which often impose very slow scanning speeds that would only rarely be used under natural conditions (e.g., Oddo et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Kinematics of unconstrained tactile texture exploration

Callier

Saal

Davis-Berg

et al. 2015

Journal of Neurophysiology

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A hallmark of tactile texture exploration is that it involves movement between skin and surface. When we scan a surface, small texture-specific vibrations are produced in the skin, and specialized cutaneous mechanoreceptors convert these vibrations into highly repeatable, precise, and informative temporal spiking patterns in tactile afferents. Both texture-elicited vibrations and afferent responses are highly dependent on exploratory kinematics, however; indeed, these dilate or contract systematically with decreases or increases in scanning speed, respectively. These profound changes in the peripheral response that accompany changes in scanning speed and other parameters of texture scanning raise the question as to whether exploratory behaviors change depending on what surface is explored or what information is sought about that surface. To address this question, we measure and analyze the kinematics as subjects explore textured surfaces to evaluate different types of texture information, namely the textures' roughness, hardness, and slipperiness. We find that the exploratory movements are dependent both on the perceptual task, as has been previously shown, but also on the texture that is scanned. We discuss the implications of our findings regarding the neural coding and perception of texture.

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Section: Discussionmentioning

confidence: 99%

Kinematics of unconstrained tactile texture exploration

Callier

Saal

Davis-Berg

et al. 2015

Journal of Neurophysiology

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“…Overall, we have 16 channels transducing mechanical stimulation applied to an area of about 22.3mm 2 of the fingerpad. The equivalent average density of input units is 72 units/cm 2 , that is close to the 70 units/cm 2 of human Merkel-SA1 mechanoreceptors and half of the approximate density of human Meissner-RA1 units, which have been shown to encode roughness [7] [12]. The 16 channels provided by the 4 sensors are directly acquired at 375 Hz by means of a high-resolution (24 bit) Analog to Digital Converter (ADS8345) interfaced to a FPGA (Altera Cyclone II), and then transmitted via ethernet protocol to a PC for processing operations.…”

Section: A Biomimetic Fingertipmentioning

confidence: 98%

“…1, consists of a 2x2 array (with a 2.36mm pitch) of integrated MEMS sensors [4] [6] [7]. Each sensor integrates 4 piezoresistors at the base of a cross-shaped structure (see Fig.…”

Section: A Biomimetic Fingertipmentioning

confidence: 99%

“…This research study addresses the understanding of the tactile system mechanoneurotransduction mechanisms [2], with the aim to emulate or substitute the human tactile capabilities by means of a robotic artifact. This topic was investigated as part of a years-long research [3] [4] which provided the framework to collect both biological data via human microneurography [5] in parallel to artificial data via a 16 channels MicroElectroMechanical System (MEMS) array touch sensor [6] [7]. In this framework, we specifically addressed the conversion of raw sensor outputs to spike codes.…”

Section: Introductionmentioning

confidence: 99%

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Soft-neuromorphic artificial touch for applications in neuro-robotics

Spigler

Oddo

Carrozza

2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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We propose an artificial mechanotransduction system based on a 2×2 MEMS array touch sensor, and evaluate a neural model which is designed to convert raw sensor outputs into neural spike-trains. We show that core tactile information is preserved in the neural representation, and that the resulting modulation via spikes can be used in surface discrimination tasks. In this research study the first neural stage (i.e., at mechanoreceptor level) of somatosensory system was mimicked in a soft-neuromorphic fashion, while future works will target the implementation of the 2nd order stage (Cuneate neurons) to further understand the biological mechanisms underlying maximum transfer and fast processing of tactile information

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“…1 It is known that roughness of the surface is determined according to the distance of each ridge λ. 2 When the sensor is scanning with constant velocity …”

Section: Working Principlementioning

confidence: 99%

Development of a biomimetic roughness sensor for tactile information with an elastomer

et al. 2016

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Human uses various sensational information for identifying an object. When contacting an unidentified object with no vision, tactile sensation provides a variety of information to perceive. Tactile sensation plays an important role to recognize a shape of surfaces from touching. In robotic fields, tactile sensation is especially meaningful. Robots can perform more accurate job using comprehensive tactile information. And in case of using sensors made by soft material like silicone, sensors can be used in various situations. So we are developing a tactile sensor with soft materials. As the conventional robot operates in a controlled environment, it is a good model to make robots more available at any circumstance that sensory systems of living things. For example, there are lots of mechanoreceptors that each of them has different roles detecting simulation in side of human skin tissue. By mimicking the mechanoreceptor, a sensory system can be realized more closely to human being. It is known that human obtains roughness information through scanning the surface with fingertips. During that times, subcutaneous mechanoreceptors detect vibration. In the same way, while a robot is scanning a surface of object, a roughness sensor developed detects vibrations generated between contacting two surfaces. In this research, a roughness sensor made by an elastomer was developed and experiment for perception of objects was conducted. We describe means to compare the roughness of objects with a newly developed sensor.

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Roughness Encoding in Human and Biomimetic Artificial Touch: Spatiotemporal Frequency Modulation and Structural Anisotropy of Fingerprints

Cited by 45 publications

References 45 publications

Kinematics of unconstrained tactile texture exploration

Kinematics of unconstrained tactile texture exploration

Soft-neuromorphic artificial touch for applications in neuro-robotics

Development of a biomimetic roughness sensor for tactile information with an elastomer

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