Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity

Kumar, Deepesh; Ghosh, Rohan; Nakagawa-Silva, Andrei; Soares, Alcimar Barbosa; Thakor, Nitish V.

doi:10.1016/j.neucom.2020.04.131

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…In a real‐time purpose, since the feedforwarding in the system for a real‐time use takes 0.9 ms in a GPU (NVIDIA Geforce GTX 1070 with Intel i7‐6700K) and 1.1 ms in a CPU (Intel i7‐6700K) processor respectively after taking sensing signal, it is feasible that it can be performed as a real‐time system, having millisecond delay. Since neuromorphic system mimics neural network in a chip, [ 56,57 ] this tactile avatar system on a neuromorphic system can reduce the time delay of the computation and minimize the size of the machine to perform in the realization.…”

Section: Resultsmentioning

confidence: 99%

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

et al. 2021

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As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s−1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

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Section: Resultsmentioning

confidence: 99%

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

et al. 2021

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“…Izhikevich neuron model, [ 132 ] with its reasonable biological plausibility, efficient computational cost, and high neuron dynamics, has been used commonly to transform analog tactile signals into spikes. [ 127,133–140 ] Integrate‐and‐fire model [ 141,142 ] and spike response model, [ 143 ] with more efficiency on computation cost than Izhikevich, have also been applied to signal conversion, [ 123,144–149 ] although they have poor biological plausibility. Usually, biological neuron models are implemented at a software level, whereas a biristor neuron was fabricated with a triboelectic nanogenerator sensor to mimic the integrate‐and‐fire function (Figure 6b).…”

Section: Machine Learning For Spike Signalsmentioning

confidence: 99%

Machine Learning for Tactile Perception: Advancements, Challenges, and Opportunities

Lin

et al. 2023

Advanced Intelligent Systems

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The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed.

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“…A tactile sensor array, similar to one used by Kumar et.al. in [30], was fabricated by sandwiching a piezoresistive fabric (LR-SLPA, Eeonyx, CA, USA) between a set of conductive electrodes (see figure 1(a)). The electrodes were printed on flexible printed circuit board substrate and were arranged in an orthogonal fashion.…”

Section: A Tactile Sensormentioning

confidence: 99%

“…Each taxel was individually sampled at 1000 Hz using a custom built data acquisition board (DAQ). The data was transferred to PC via USB communication [30].…”

Section: A Tactile Sensormentioning

confidence: 99%

Spatio-temporal encoding improves neuromorphic tactile texture classification

Gupta,

Nakagawa,

Lepora

et al. 2020

Preprint

Self Cite

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With the increase in interest in deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important. In this work, we implement a multichannel neuromorphic tactile system that encodes contact events as discrete spike events that mimic the behavior of slow adapting mechanoreceptors. We study the impact of information pooling across artificial mechanoreceptors on classification performance of spatially non-uniform naturalistic textures. We encoded the spatio-temporal activation patterns of mechanoreceptors through gray-level co-occurrence matrix computed from time-varying mean spiking ratebased tactile response volume. We found that this approach greatly improved texture classification in comparison to use of individual mechanoreceptor response alone. In addition, the performance was also more robust to changes in sliding velocity. The importance of exploiting precise spatial and temporal correlations between sensory channels is evident from the fact that on either removal of precise temporal information or altering of spatial structure of response pattern, a significant performance drop was observed. This study thus demonstrates the superiority of population coding approaches that can exploit the precise spatio-temporal information encoded in activation patterns of mechanoreceptor populations. It, therefore, makes an advance in the direction of development of bio-inspired tactile systems required for realistic touch applications in robotics and prostheses.

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Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity

Cited by 9 publications

References 29 publications

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

Machine Learning for Tactile Perception: Advancements, Challenges, and Opportunities

Spatio-temporal encoding improves neuromorphic tactile texture classification

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