Data Analyzing and Daily Activity Learning with Hidden Markov Model

摘要：

To observe and analyze persons daily activities, and build the activities model is an important task in an intelligent environment In an Ambient Assisted Living (AAL) project we get sensor data from a motion detector. At first we translate and reduce the raw data to state data. Secondly using hidden Markov model, forward algorithm, and Viterbi Algorithm to analyze the data and build the persons daily activity model. Comparing individual observation with the build model to find out best and worst (abnormal) activities.

关键词： intelligent environment hidden markov model (HMM) forward algorithm viterbi algorithm

作者: GuoQing Yin Dietmar Bruckner

作者单位: Institute of Computer Technology Vienna University of Technology, Austria, Europe

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 380-384

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）