HMM-BASED TRI-TRAINING ALGORITHM IN HUMAN ACTIVITY RECOGNITION WITH SMARTPHONE

摘要：

　　With the popularity of smartphone,studies using sensors on smartphone have been investigated in recent years.Human activity recognition is one of the active research topics.Users context can be used for providing users the adaptive services and the advice about health based on a stream of activity data.In this paper,we introduce a HMM-based Tri-training algorithm.The Tri-training algorithm can automatically augment activity classifiers after they are deployed in a real environment.HMM model can use the relationship between previous and current states to help Tri-training algorithm chooses new samples for training set.This method can explicitly reduce the amount of noise introduction into classifier group and make the output state stream connect more smoothly.

关键词： Semi-supervised Hidden markov model Tri-training learning Activity recognition

作者: Bin Xie Qing Wu

作者单位: Hangzhou Dianzi University,Hangzhou 310018,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 138-142

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）