摘要
目前,在人体行为识别领域中,分类模型通常有两种,分别是通用模型和个性化模型。但是通用模型没有考虑人体多样性问题,不能适用于所有人,而个性化模型需要人为干预较多,针对这两种模型的不足提出了一种折中的模型训练方法,即对人体多样性因素分区间后的原始加速度数据进行训练得到多个模型。另外,为了使识别模型适用于更加广泛的情况,在数据采集阶段还考虑了加速度传感器的位置。该方法使得模型更具普遍性的同时又能够提高识别精确度,通过对人体静止、走路、跑步、上下楼梯五种行为进行试验,识别率达到了95%左右。实验表明该方法是切实有效的。
At present,in the field of human action recognition,there are generally two classification models：the general model and the personalized model.But the diversity of human bodies is not considered in the general model,so it is not suitable for everyone.On the other hand,the personalized model needs more human intervention.In order to make up the deficiency of the two models,the paper proposes a compromise model training method which trains the raw acceleration data after partitioning the diversified factors of human bodies to obtain multiple models.Additionally,the position of the acceleration sensor has been taken into consideration during the process of data collection,in order to extend the application scope of the recognition model.This method provides the model with better universality and recognition accuracy.Eventually,through the test on the five human actions of standing,walking,running,going up and down the stairs,the recognition rate reaches about 95%.Experiments show that the method is practical and effective.
作者
余杰
杨连贺
焦帅
易明雨
于佃存
YU Jie YANG Lianhe JIAO Shuai YI Mingyu yu Diancun(School of Computer Science ＆ Software Engineering, Tianjin Polytechnic University, Tianjin 300387,China Research Center for Pervasive Computing,Institute of Computing Technology,Chinese Academy of Sciences, Beijing 100190,China The College of Information Engineering of Xiangtan University,Xiangtan 411105 Sol,rare College of Shandong University,Jinan 250101 ,China)
出处
《软件工程》
2016年第9期34-37,共4页
Software Engineering
关键词
人体多样性
行为识别
模型
位置
精确度
diversity of human bodies
action recognition
model
position
accuracy
