Journal
NEURAL NETWORKS
Volume 53, Issue -, Pages 1-7Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2014.01.008
Keywords
Extreme learning machine; Reduced kernel extreme learning machine; Activity recognition; Support vector machine
Funding
- Innovation fund research group [61221063]
- National Science Foundation of China [61100166, 91118005, 91218301, 61373116]
- National High Technology Research and Development Program 863 of China [2012AA011003]
- Cheung Kong Scholars Program
- Key Projects in the National Science and Technology Pillar Program [2011BAK08B01, 2012BAH16F02, 2013BAK09B01]
- Shaanxi provincial youth science and technology star plan [2013Kjxx-29]
- Shaanxi province ordinary high school
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Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance. 2014 Elsevier Ltd. All rights reserved.
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