Earticle

초록

영어

Human activity recognition is a main research area of context-aware computing, and is widely used in many applications, such as smart home and elderly care. Smart phone-based human activity recognition is very popular by making use of the embedded inertial sensors. However, there exists the problems of misclassification activities, and how to effectively apply the model trained by known users to new users. To solve these two problems, in this paper, we proposed a novel approach, Uncertainty Sampling based posterior Probability Extreme Learning Machine (USP-ELM), by introducing two strategies: first, we transfer the actual outputs of ELM to posterior probabilities for each instances, and then use uncertainty sampling strategy for confidence level assignment to adapt the training model and improve the classification accuracy. Experimental results show that the proposed approach is more efficient, compared with the existing ELMs.

목차

Abstract
 1. Introduction
 2. Related Work and Preliminary
  2.1. Related Work
  2.2. Extreme Learning Machine
 3. Proposed Approach
  3.1. Posterior Probability Extreme Learning Machine
  3.2. Uncertainty Sampling
 4. Experimental Results
  4.1. Experimental Dataset and Feature Selection
  4.2. Improve the Recognition Performance with Posterior Probability ELM
  4.3. Improve the Recognition Performance with Posterior Probability ELM
 5. Conclusion and Future Work
 References

키워드

저자

• Xiaopeng Niu [ School of Computer and Communication Engineering, University of Science and Technology Beijing, China ] Corresponding Author
• Zhiliang Wang [ School of Computer and Communication Engineering, University of Science and Technology Beijing, China ]
• Wendong Xiao [ School of Automation and Electrical Engineering, University of Science and Technology Beijing, China ]
• Zhigeng Pan [ Digital Media and HCI Center, Hangzhou Normal University, Hangzhou, 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