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一种基于掌压和关节弯曲应变感知的物体识别触觉手套

张学锋 张少杰 陈鑫 张锦华

测试科学与仪器2025,Vol.16Issue(2):173-185,13.
测试科学与仪器2025,Vol.16Issue(2):173-185,13.DOI:10.62756/jmsi.1674-8042.2025017

一种基于掌压和关节弯曲应变感知的物体识别触觉手套

A tactile glove for object recognition based on palmar pressure and joint bending strain sensing

张学锋 1张少杰 1陈鑫 1张锦华1

作者信息

  • 1. 西安建筑科技大学 机电工程学院,陕西 西安 710055
  • 折叠

摘要

Abstract

With the rapid development of flexible electronics,the tactile systems for object recognition are becoming increasingly delicate.This paper presents the design of a tactile glove for object recognition,integrating 243 palm pressure units and 126 finger joint strain units that are implemented by piezoresistive Velostat film.The palm pressure and joint bending strain data from the glove were collected using a two-dimensional resistance array scanning circuit and further converted into tactile images with a resolution of 32×32.To verify the effect of tactile data types on recognition precision,three datasets of tactile images were respectively built by palm pressure data,joint bending strain data,and a tactile data combing of both palm pressure and joint bending strain.An improved residual convolutional neural network(CNN)model,SP-ResNet,was developed by light-weighting ResNet-18 to classify these tactile images.Experimental results show that the data collection method combining palm pressure and joint bending strain demonstrates a 4.33%improvement in recognition precision compared to the best results obtained by using only palm pressure or joint bending strain.The recognition precision of 95.50%for 16 objects can be achieved by the presented tactile glove with SP-ResNet of less computation cost.The presented tactile system can serve as a sensing platform for intelligent prosthetics and robot grippers.

关键词

触觉手套/物体识别/Velostat/关节弯曲应变传感器/手掌压力传感器/卷积神经网络

Key words

tactile glove/object recognition/Velostat/joint bending strain sensors/palmar pressure sensors/convolutional neural network

引用本文复制引用

张学锋,张少杰,陈鑫,张锦华..一种基于掌压和关节弯曲应变感知的物体识别触觉手套[J].测试科学与仪器,2025,16(2):173-185,13.

基金项目

This work was supported by the Key Research and Development Program of Shaanxi Province(No.2024GX-YBXM-178),and the Shaanxi Province Qinchuangyuan"Scientists+Engineers"Team Development(No.2022KXJ032) (No.2024GX-YBXM-178)

测试科学与仪器

1674-8042

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