An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition

Abstract

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Soft eSkin: distributed touch sensing with harmonized energy and computing

“…The active matrix of sensor-based approaches [128][129][130][131] is proposed as potential solutions for rapid and reliable processing of tactile data. In this regard, the event-driven spiking neural mechanisms (similar to the spike in the human nervous system) are relevant [17,76,[132][133][134][135][136]. The neuromorphic sensors and computing architectures with various algorithms, including linear discriminant analysis, support vector machine, spiking neural network (SNN), extreme learning machine, K-nearest neighbours and Bayesian analysis, have attracted global attention [17,76,[132][133][134][135][136].…”

“…In this regard, the event-driven spiking neural mechanisms (similar to the spike in the human nervous system) are relevant [17,76,[132][133][134][135][136]. The neuromorphic sensors and computing architectures with various algorithms, including linear discriminant analysis, support vector machine, spiking neural network (SNN), extreme learning machine, K-nearest neighbours and Bayesian analysis, have attracted global attention [17,76,[132][133][134][135][136]. As discussed previously, tactile neuromorphic systems require a different approach when compared with neuromorphic systems implemented for auditory and vision [10,30,137].…”

Patient-Adapted and Inter-Patient Ecg Classification Using Neural Network and Gradient Boosting

“…At present, the prediction method of short-term wind speed is mainly based on historical data. These prediction methods usually use historical data, through some linear models include autoregressive moving average model (ARMA) [9,34], autoregressive integrated moving average model (ARIMA) [2]. The nonlinear model include SVM [8,12], LSSVM [36,39], artificial neural network (Elman neural network [44,45], echo state network [38], fuzzy neural network [6,30], RBF neural network [4,23], and etc to predict short-term wind speed.…”

“…The authors pointed out that computing time of ELM is usually several thousand times faster than BP neural network or SVM [37]. Therefore, the ELM algorithm is applied to many classification and regression prediction problems [32][33][34]47].…”

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

“…At present, the prediction method of short-term wind speed is mainly based on historical data. These prediction methods usually use historical data, through some linear models include autoregressive moving average model (ARMA) [9,34], autoregressive integrated moving average model (ARIMA) [2]. The nonlinear model include SVM [8,12], LSSVM [36,39], artificial neural network (Elman neural network [44,45], echo state network [38], fuzzy neural network [6,30], RBF neural network [4,23], and etc to predict short-term wind speed.…”