Earticle

초록

영어

Hidden Markov Model is an important approach applied to activity recognition. In the first- order Hidden Markov Model, there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model, which debases the precision of information extraction comparatively. In second- order Hidden Markov Model, the relevance between the current state and its previous two states is considered. Also, the relevance between the current observation and its previous state is considered. So second-order Hidden Markov Model has stronger performance of recognition of incorrect information. In my paper, second-order Hidden Markov Model is applied to activity recognition. The experiments show that our approach has higher precision than those approaches based on first-order Hidden Markov Model and based on Conditional Random Fields.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Activity Recognition in Smart Homes
 4. Second-Order Hidden Markov Model and its Application in Activity Recognition
  4.1. One-Order Hidden Markov Model (HMM(1))
  4.2. Second-Order Hidden Markov Model(HMM(2))
  4.3. Viterbi(2) Algorithm
  4.4. Algorithm of Activity Recognition
 5. Experiments
  5.1. Raw Data for Experiments
  5.2 Measurement Criteria
  5.3. Experiment Result
 6. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Chunguang Zhang [ School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China ]
• Lifang Zhang [ School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605