Identifying the confidence level of activity recognition via HMM

A context-based method to identify the confidence level of activ recognition was proposed,referred to as S-HMM(sliding window hidden Markov model),which reduced the confusion rate and facilitated the transfer learning.With S-HMM,the activity recognition sequence was modeled as HMM(hidden Markov mode...

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Main Authors: Chang-hai WANG, Jian-zhong ZHANG, Jing-dong XU, Yu-wei XU
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2016-05-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016102/
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author Chang-hai WANG
Jian-zhong ZHANG
Jing-dong XU
Yu-wei XU
author_facet Chang-hai WANG
Jian-zhong ZHANG
Jing-dong XU
Yu-wei XU
author_sort Chang-hai WANG
collection DOAJ
description A context-based method to identify the confidence level of activ recognition was proposed,referred to as S-HMM(sliding window hidden Markov model),which reduced the confusion rate and facilitated the transfer learning.With S-HMM,the activity recognition sequence was modeled as HMM(hidden Markov model)and the corresponding probability was adopted as the confidence level.This ,S-HMM removed the dependency of the confidence level on the sample distribution in the feature space.S-HMM is extensively evaluated based on real-life activity data,demonstrat-ing a reduced confusion rate of 37% when compared to the state-of-the-art methods.
format Article
id doaj-art-1905d6d7a28d4e57a609e45a0c226fed
institution OA Journals
issn 1000-436X
language zho
publishDate 2016-05-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-1905d6d7a28d4e57a609e45a0c226fed2025-08-20T02:34:39ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2016-05-013714315159701203Identifying the confidence level of activity recognition via HMMChang-hai WANGJian-zhong ZHANGJing-dong XUYu-wei XUA context-based method to identify the confidence level of activ recognition was proposed,referred to as S-HMM(sliding window hidden Markov model),which reduced the confusion rate and facilitated the transfer learning.With S-HMM,the activity recognition sequence was modeled as HMM(hidden Markov model)and the corresponding probability was adopted as the confidence level.This ,S-HMM removed the dependency of the confidence level on the sample distribution in the feature space.S-HMM is extensively evaluated based on real-life activity data,demonstrat-ing a reduced confusion rate of 37% when compared to the state-of-the-art methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016102/activity recognitionhidden Markov modelconfusion rateconfidence level
spellingShingle Chang-hai WANG
Jian-zhong ZHANG
Jing-dong XU
Yu-wei XU
Identifying the confidence level of activity recognition via HMM
Tongxin xuebao
activity recognition
hidden Markov model
confusion rate
confidence level
title Identifying the confidence level of activity recognition via HMM
title_full Identifying the confidence level of activity recognition via HMM
title_fullStr Identifying the confidence level of activity recognition via HMM
title_full_unstemmed Identifying the confidence level of activity recognition via HMM
title_short Identifying the confidence level of activity recognition via HMM
title_sort identifying the confidence level of activity recognition via hmm
topic activity recognition
hidden Markov model
confusion rate
confidence level
url http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016102/
work_keys_str_mv AT changhaiwang identifyingtheconfidencelevelofactivityrecognitionviahmm
AT jianzhongzhang identifyingtheconfidencelevelofactivityrecognitionviahmm
AT jingdongxu identifyingtheconfidencelevelofactivityrecognitionviahmm
AT yuweixu identifyingtheconfidencelevelofactivityrecognitionviahmm