Final Report Summary - NANOBIOTACT (Nano-engineering biomimetic tactile sensors)
The overall Science and technology (S&T) objective of the NANOBIOTACT project was to develop a biomimetic tactile sensor for incorporation in the finger pad of an artificial finger. This was done by developing sufficient scientific understanding of the operational characteristics of the human mechano-receptors, the factors that influence their recruitment and discharge patterns, and the human neural coding of taction.
Theories explaining the role of key variables affecting texture perception in healthy and sensory impaired participants were reviewed in terms of neurophysiology and psychology. The concept of 'touch' was elaborated in terms of the psychophysical tasks and methods required to evaluate and discriminate textures and the influence of tribology of skin including the effect of lubrication. Tactile stimuli were then identified with gratings being the main example since their topography can be precisely defined.
Procedures in psychophysics allowed the estimation of texture magnitude, and the measurement of roughness discrimination thresholds, tactile spatial acuity, discrimination thresholds for vibratory stimuli, slipperiness and viscosity. The subject groups included adolescents (median age 15), young adults (median age 25), older adults (median age 60) and patients with peripheral and central lesions. In vivo studies of the influence of applied normal load and velocity on tactile systems resulted in an understanding of the complex frictional behaviour of the finger pad, which is critical in tactile perception.
The work included such factors as the hydrophobicity / hydrophilicity, roughness and porosity of the tactile surface, the state of hydration of the finger pad and lubricants of different viscosities.
Microneurography was used to investigate the peripheral mechanisms of texture discrimination in healthy human volunteers. The responses of single tactile afferents innervating the skin of the finger tips were recorded during stimulation with different types of surfaces, and the responses in relation to varying tactile stimulus parameters such as surface texture, normal force or sliding velocity across the fingertip were explored.
Two artificial tactile sensors were developed based on a Microelectromechanical system (MEMS) sensor concept, i.e. a Micro-three-axis-force (MicroTAF) sensor with four integrated piezoresistors to detect the three components of an external applied force and more advanced capacitive artificial tactile arrays.
Packaging materials were based primarily on un-layered and layered elastomers with artificial finger prints and also tissue engineered skin was developed for this purpose. The artificial fingerprints were incorporated to promote vibrations and hence achieve spectral selection and amplification of tactile information as in the human finger, and, several variations of the design were implemented in order to investigate the role of fingerprints in the encoding of fine textures.
The processing of microneurography data has led to more insights into the discriminative power of the single afferents regarding velocity discrimination and the potential applicability for robust afferent type classification after a few strokes with the texture stimulus. This also includes new findings regarding the importance of spike timing in the neural code. The application of such advanced machine learning codes also established the high classification accuracy of the virtual finger and biomimetic finger for both gratings and textiles.
Theories explaining the role of key variables affecting texture perception in healthy and sensory impaired participants were reviewed in terms of neurophysiology and psychology. The concept of 'touch' was elaborated in terms of the psychophysical tasks and methods required to evaluate and discriminate textures and the influence of tribology of skin including the effect of lubrication. Tactile stimuli were then identified with gratings being the main example since their topography can be precisely defined.
Procedures in psychophysics allowed the estimation of texture magnitude, and the measurement of roughness discrimination thresholds, tactile spatial acuity, discrimination thresholds for vibratory stimuli, slipperiness and viscosity. The subject groups included adolescents (median age 15), young adults (median age 25), older adults (median age 60) and patients with peripheral and central lesions. In vivo studies of the influence of applied normal load and velocity on tactile systems resulted in an understanding of the complex frictional behaviour of the finger pad, which is critical in tactile perception.
The work included such factors as the hydrophobicity / hydrophilicity, roughness and porosity of the tactile surface, the state of hydration of the finger pad and lubricants of different viscosities.
Microneurography was used to investigate the peripheral mechanisms of texture discrimination in healthy human volunteers. The responses of single tactile afferents innervating the skin of the finger tips were recorded during stimulation with different types of surfaces, and the responses in relation to varying tactile stimulus parameters such as surface texture, normal force or sliding velocity across the fingertip were explored.
Two artificial tactile sensors were developed based on a Microelectromechanical system (MEMS) sensor concept, i.e. a Micro-three-axis-force (MicroTAF) sensor with four integrated piezoresistors to detect the three components of an external applied force and more advanced capacitive artificial tactile arrays.
Packaging materials were based primarily on un-layered and layered elastomers with artificial finger prints and also tissue engineered skin was developed for this purpose. The artificial fingerprints were incorporated to promote vibrations and hence achieve spectral selection and amplification of tactile information as in the human finger, and, several variations of the design were implemented in order to investigate the role of fingerprints in the encoding of fine textures.
The processing of microneurography data has led to more insights into the discriminative power of the single afferents regarding velocity discrimination and the potential applicability for robust afferent type classification after a few strokes with the texture stimulus. This also includes new findings regarding the importance of spike timing in the neural code. The application of such advanced machine learning codes also established the high classification accuracy of the virtual finger and biomimetic finger for both gratings and textiles.
