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

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.