Processing IMU action recognition based on brain-inspired computing with microfabricated MEMS resonators

Abstract

Reservoir Computing (RC) decomposes the recurrent neural network into a fixed network with recursive connections and a trainable linear network. With the advantages of low training cost and easy hardware implementation, it provides a method for the effective processing of time-domain correlation information. In this paper, we build a hardware RC system with a nonlinear MEMS resonator and build an action recognition data set with time-domain correlation. Moreover, two different universal data set are utilized to verify the classification and prediction performance of the RC hardware system. At the same time, the feasibility of the novel data set was validated by three general machine learning approaches. Specifically, the processing of this novel time-domain correlation data set obtained a relatively high success rate. These results, together with the dataset that we build, enable the broad implementation of brain-inspired computing with microfabricated devices, and shed light on the potential for the realization of integrated perception and calculation in our future work.

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge

“…Although the proposed MEMS RC has not yet been tested in a real-world acceleration or temperature application scenario due to non-wearable conditions, the virtual acceleration experiment demonstrates equivalent system capabilities. Moreover, our MEMS RC has been validated in two scenarios in our previous work: acceleration recognition of IMU motions 15 , 30 , and temperature compensation for MEMS resonators 20 . Our work advances the development of sensing-computing integration in the IoT fields, presenting a novel sensing paradigm for MEMS devices 31 .…”

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge

“…Although the proposed MEMS RC has not yet been tested in a real-world acceleration or temperature application scenario due to non-wearable conditions, the virtual acceleration experiment demonstrates equivalent system capabilities. Moreover, our MEMS RC has been validated in two scenarios in our previous work: acceleration recognition of IMU motions 15,28 , and temperature compensation for MEMS resonators 19 . Our work advances the development of sensingcomputing integration in the IoT fields, presenting a novel sensing paradigm for MEMS devices.…”

A sensing paradigm of analog-to-feature (A2F) based on the MEMS accelerometer-integrated reservoir computing

“…This feature extraction is carried out by the dynamics of a nonlinear system with xed-weight connections, called a reservoir. Moreover, the xed reservoir without adaptive updating is amenable to hardware implementation using various nonlinear dynamical systems 18 , such as electronics 19,20 , optoelectronics [21][22][23][24] , spintronics [25][26][27] , electrochemical systems 28,29 , memristors 7,[30][31][32] , and MEMS devices [33][34][35][36][37][38][39] . Therein, MEMS-based RC serves the dual abilities of sensing and computing for various force stimuli (accelerations, sound pressure, etc.…”