70 research outputs found
A Biomimetic MEMS-based Tactile Sensor Array with Fingerprints integrated in a Robotic Fingertip for Artificial Roughness Encoding
No full textThis work shows the accomplishment of a full integration of a biomimetic 2 à 2 tactile array and related electronics in an artificial fingertip. The technological approach is based on merging 3D MEMS sensors and skin-like artificial materials that are moulded mimicking human epidermal ridges. Experimental results using a mechatronic tactile stimulator for indenting periodic gratings (spatial periodicity from 400 ¿m to 1900 ¿m) and sliding them at constant speeds (from 5 mm/s to 40 mm/s) under regulated normal contact forces (between 100 mN and 400 mN) show that the developed sensing technology is suitable for fine roughness encoding: a frequency shift of the principal spectral component arising from sensor outputs was observed coherently with the spatial periodicity of the used ridged stimuli and their sliding velocity. Such phenomenon is pointed out with fine gratings particularly when the stimulation is operated along the proximal-distal direction of the finger (i.e. with sliding motion of the ridges of the stimulus across the ridges of the packaging) showing a more marked frequency locked behavior if compared to the radial-ulnar stimulation (i.e. with sliding motion of the ridges of the grating along the ridges of the packaging)
Selective stimulation with intraneural electrodes for bionic limb prostheses can contribute to shed light on human touch sensorimotor integration
No full textDecoding of naturalistic textures from spike patterns of neuromorphic artificial mechanoreceptors
No full textSoft-neuromorphic artificial touch for applications in neuro-robotics
No full textWe 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
Artificial Roughness Encoding with a Bio-inspired MEMS-based Tactile Sensor Array
No full textA compliant 2x2 tactile sensor array was developed and investigated for roughness encoding. State of the art cross shape 3D MEMS sensors were integrated with polymeric packaging providing in total 16 sensitive elements to external mechanical stimuli in an area of about 20 mm2, similarly to the SA1 innervation density in humans. Experimental analysis of the bio-inspired tactile sensor array was performed by using ridged surfaces, with spatial periods from 2.6 mm to 4.1 mm, which were indented with regulated 1N normal force and stroked at constant sliding velocity from 15 mm/s to 48 mm/s. A repeatable and expected frequency shift of the sensor outputs depending on the applied stimulus and on its scanning velocity was observed between 3.66 Hz and 18.46 Hz with an overall maximum error of 1.7%. The tactile sensor could also perform contact imaging during static stimulus indentation. The experiments demonstrated the suitability of this approach for the design of a roughness encoding tactile sensor for an artificial fingerpad
Slippage Detection with Piezoresistive Tactile Sensors
No full textOne of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset
Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions
No full textWe implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications
Vision-based pose estimation for robot-mediated hand telerehabilitation
No full textVision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic system, without requiring complex calibration procedures. Moreover, VPE interfaces are gaining momentum as they are highly intuitive, such that they can be used from untrained personnel (e.g., a generic caregiver) even in delicate tasks as rehabilitation exercises. In this paper, we present a novel master-slave setup for hand telerehabilitation with an intuitive and simple interface for remote control of a wearable hand exoskeleton, named HX. While performing rehabilitative exercises, the master unit evaluates the 3D position of a human operator’s hand joints in real-time using only a RGB-D camera, and commands remotely the slave exoskeleton. Within the slave unit, the exoskeleton replicates hand movements and an external grip sensor records interaction forces, that are fed back to the operator-therapist, allowing a direct real-time assessment of the rehabilitative task. Experimental data collected with an operator and six volunteers are provided to show the feasibility of the proposed system and its performances. The results demonstrate that, leveraging on our system, the operator was able to directly control volunteers’ hands movements
Spatiotemporal Dynamics of the Cortical Responses Induced by a Prolonged Tactile Stimulation of the Human Fingertips
No full textThe sense of touch is fundamental for daily behavior. The aim of this work is to understand the neural network responsible for touch processing during a prolonged tactile stimulation, delivered by means of a mechatronic platform by passively sliding a ridged surface under the subject’s fingertip while recording the electroencephalogram (EEG). We then analyzed: (i) the temporal features of the Somatosensory Evoked Potentials and their topographical distribution bilaterally across the cortex; (ii) the associated temporal modulation of the EEG frequency bands. Long-latency SEP were identified with the following physiological sequence P100—N140—P240. P100 and N140 were bilateral potentials with higher amplitude in the contralateral hemisphere and with delayed latency in the ipsilateral side. Moreover, we found a late potential elicited around 200 ms after the stimulation was stopped, which likely encoded the end of tactile input. The analysis of cortical oscillations indicated an initial increase in the power of theta band (4–7 Hz) for 500 ms after the stimulus onset followed a decrease in the power of the alpha band (8–15 Hz) that lasted for the remainder of stimulation. This decrease was prominent in the somatosensory cortex and equally distributed in both contralateral and ipsilateral hemispheres. This study shows that prolonged stimulation of the human fingertip engages the cortex in widespread bilateral processing of tactile information, with different modulations of the theta and alpha bands across time
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