典型文献
Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton
文献摘要：
The wearable lower limb exoskeleton is a typical human-in-loop human-robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.
文献关键词：
中图分类号：
作者姓名：
Jiaqi Wang;Dongmei Wu;Yongzhuo Gao;Xinrui Wang;Xiaoqi Li;Guoqiang Xu;Wei Dong
作者机构：
State Key Laboratory of Robotics and System,Harbin Institute of Technology,Harbin 150001,China;Weapon Equipment Research Institute,China South Industries Group Corporation,Beijing 102202,China
文献出处：
引用格式：
[1]Jiaqi Wang;Dongmei Wu;Yongzhuo Gao;Xinrui Wang;Xiaoqi Li;Guoqiang Xu;Wei Dong-.Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton)[J].仿生工程学报（英文版）,2022(05):1359-1373
A类：
Exoskeleton,recognizer
B类：
Integral,Real,Locomotion,Mode,Recognition,Based,GA,Lower,Limb,wearable,lower,limb,exoskeleton,typical,human,loop,robot,coupled,system,which,conducts,natural,close,cooperation,by,recognizing,locomotion,timely,Requiring,specific,training,main,challenge,existing,approaches,most,methods,have,problem,insufficient,recognition,This,paper,proposes,integral,adaptive,real,LMR,combination,Convolutional,Neural,Networks,Genetic,Algorithm,multi,information,selection,To,improve,network,performance,hyper,parameters,are,optimized,Bayesian,optimization,An,prototype,sensors,novel,sensing,shoes,used,verify,proposed,Twelve,modes,composed,daily,application,can,be,recognized,According,series,experiments,shows,strong,comprehensive,abilities,including,high,accuracy,delay,adaption,different,subjects
AB值：
0.612004
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