摘要
为克服步态辨识中特征向量法存在的步态特征难于提取、计算量大和算法复杂等局限,提出一种基于过程神经网络的步态模式自动分类综合方案.为感知人体步态,在测试者下肢安装加速度传感器来采集步行过程中的时序运动学信息.采用巴特沃斯滤波处理并将其拟合为时变函数直接输入到过程神经网络,利用其对任意连续泛函的逼近能力来实现对不同步态模式下时序加速度信号的自动分类.同时,针对传统梯度下降法难于收敛和局部极小等问题,提出采用粒子群优化算法对网络的权函数和权系数进行学习.实验证明了该方案的正确性和有效性.
A general scheme for automated classification of gait patterns based on process neural networks is proposed to overcome limitations of the feature vector approach,including difficulty in feature selection,heavy computing load,and complexity of recognition algorithms.Initial human gait capture was done using accelerometers mounted on the lower limbs of a subject to obtain kinematic information for various gait patterns.Using a Butterworth filter,a time-series acceleration signal was processed,then fitted as a time-dependent function and presented to PNNs directly.For the capacity of arbitrary functional approximation of PNNs,the automated classification of time-series acceleration signals for different gait patterns was achieved.Furthermore,to overcome the slow convergence and localized minimum problems with the traditional gradient descent method,a particle swarm optimization algorithm was adopted to modify the weight functions and weight values of PNNs.Experimental results demonstrated the correctness and effectiveness of the proposed scheme.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第4期464-467,471,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60705031)
教育部高等学校博士学科点专项科研资金资助项目(20070145105)
中央高校基本科研业务费专项资金资助项目(N090404007)
关键词
步态模式分类
加速度测量
过程神经网络
正交基函数
粒子群优化
gait pattern classification
accelerometry
process neural network
orthogonal basis function
particle swarm optimization
