基于卷积神经网络的人体行为识别方法  

Human Behavior Recognition Method Based on Convolutional Neural Network

作　　者：林峰[1] LIN Feng(Fuzhou Polytechnic,Fuzhou 350108,China)

机构地区：[1]福州职业技术学院信息工程系,福建福州350108

出　　处：《通化师范学院学报》2023年第12期61-66,共6页Journal of Tonghua Normal University

基　　金：福建省中青年教师教育科研项目(JAT210813)。

摘　　要：为提高基于智能手机三轴加速度传感器的人体行为分类模型准确率,提出了一种卷积神经网络人体行为识别方法,通过固定时间窗口对连续样本数据进行切割分片,构造多层神经元网络结构,优化调整关键核心参数,使用新方法训练出的人体行为识别模型平均交叉准确率可达91.7%,较其他传统机器学习算法有较大提升.In order to improve the accuracy of human behavior recognition based on the smartphone's three-axis acceleration sensor,this paper proposes a human behavior recognition method based on convolutional neural network.This method cuts and slices continuous sample data through a fixed time window,constructs a multi-layer neuron network structure,optimizes and adjusts key core parameters.The average crossover accuracy of the trained human behavior recognition model can reach 91.7%,which is higher than other traditional machine learning.

关 键 词：人体行为识别 卷积神经网络 三轴加速度传感器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]