摘要
针对身份识别领域足底压力采集设备在传感器数量和位置配置方面存在的差异性,以及足底压力特征通常依赖于对步态周期数据进行完整分割所带来的时间成本增加问题,提出一种基于足底压力信号的无阈值递归图和LeNet网络的身份识别方法。首先使用自制足底压力采集设备,在常规混凝土地面采集28名无足部及下肢疾病的健康成年参与者无负重等干扰状态自然行走过程中的足底压力数据;再经数据重构算法对足底压力数据进行预处理,将其转化为无阈值递归图;最后将生成的图像作为LeNet网络的输入,完成特征提取与身份识别,并对单一区域及多区域组合方案的结果进行分析比较。实验结果表明,足跟内侧区域、足跟外侧区域、第二跖骨区域和大脚趾区域的组合身份识别性能以最少的传感器数量和高识别精度优于其他方案,其中准确率、精确率、召回率和F 1分数分别达到99.25%、99.22%、99.39%、99.26%。不同区域的身份识别性能受行走过程中不同阶段和受力大小的影响,但随着区域数量的增加,该影响逐渐减弱。此外,实验结果还显示,使用足底压力信号的无阈值递归图进行身份识别的方法无需依赖严格的步态分割,依然能够保持较高的识别精度。为身份识别技术在生物特征识别领域的应用提供了新的思路与技术支持,在公共安全等领域具有潜在的应用价值。
Aiming at the variability in the number and positional configuration of sensors in plantar pressure acquisition devices within the field of identity recognition,as well as the issue of increased time cost arising from the conventional reliance on complete segmentation of gait cycle data for extracting plantar pressure features,this article proposes an identity recognition method based on threshold-free recurrence plots of plantar pressure signals and the LeNet neural network.Plantar pressure data are collected during natural,unloaded walking from 28 healthy adult participants without foot or lower limb pathologies.Data acquisition occurs on a standardized concrete surface using a custom-designed plantar pressure measurement system.The raw plantar pressure data are preprocessed using a data reconstruction algorithm to directly convert them into threshold-free recurrence plots.These generated images are then used as input to the LeNet network for feature extraction and identity recognition.Recognition performance across various single-region and multi-region configurations is systematically analyzed and compared.Experimental results show that the optimal configuration—combining the medial heel,lateral heel,second metatarsal head,and hallux regions—achieved superior identity recognition performance with minimal sensor deployment and high accuracy.Specifically,accuracy,precision,recall and F 1-score attained 99.25%,99.22%,99.39%,and 99.26%,respectively.Recognition performance tends to be influenced by gait phase and plantar pressure magnitude.However,this effect progressively diminishes with increasing number of integrated regions.Furthermore,the proposed method maintains high recognition accuracy without requiring rigorous gait segmentation.It provides new ideas and technical support for the application of identity recognition technology in the field of biometric identification,and has potential application value in public safety and other fields.
作者
袁田
辛义忠
Yuan Tian;Xin Yizhong(School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110142,China)
出处
《仪器仪表学报》
北大核心
2025年第6期338-347,共10页
Chinese Journal of Scientific Instrument
基金
辽阳市“辽阳英才计划”“带土移植”创新团队项目(LYYC220501)资助。
关键词
足底压力特征
无阈值递归图
LeNet网络
身份识别
plantar pressure characteristics
threshold-free recurrence plot
LeNet network
identity recognition
