Analog Sensing and Computing Systems with Low Power Consumption for Gesture Recognition. (16th November 2020)

• Record Type: Journal Article
• Authors: Fan, Tianyi; Liu, Zheyu; Luo, Zewei; Li, Junda; Tian, Xiyue; Chen, Yeshen; Feng, Yuan; Wang, Chaolun; Bi, Hengchang; Li, Xinming; Qiao, Fei; Wu, Xing
• Is Part Of: Advanced intelligent systems. Volume 3:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-16
• Subjects: artificial intelligence -- gesture recognitions -- low power consumptions -- pressure sensors; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202000184
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Traditional general architecture chips have shown excessive power consumption and insufficient functional redundancy in some customized applications. Flexible electronics also call for customized chips in smart wearable devices. Common chips in intelligent systems process digital signals, and continuous operation of the system clock brings great power consumption. Herein, an intelligent gesture recognition system is developed by combining the flexible 2D materials and an analog computing chip. The pressure‐sensitive sponge with graphene fillings is proposed as a piezoresistive sensor. A nine‐sensor array is used to detect the pressure field distribution caused by hand movement. To get rid of the power consumption caused by the system clock, a novel analog‐computing customized chip which adopts a near‐sensor processing architecture is proposed. It implements the binary neural network algorithm with an analog circuit and completes the recognition of the transmission signal at the hardware level. The chip possesses a low power consumption which is less than 1.8 mW. Moreover, a glove assembled by highly pressure‐sensitive material recognizes mute gestures including Arabic numerals 0–9, with a recognition rate as high as 98.5%. Herein, the prospects for the application of customized smart chips in the field of smart wearable electronics are illuminated. Abstract : A chip based on a near‐sensor processing architecture is fabricated to assemble with pressure sensors and directly process the perceptive signals. With the design of analog multiply‐accumulate operation inside the chip, a binarize neural network algorithm is implemented to compute data from sensors and recognize various gestures including Arabic numerals, and its recognition rate is up to 98.5%.