Cross-person activity recognition using reduced kernel extreme learning machine |
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| Institution: | 1. School of Computer Science and Technology, Xian University of Posts & Telecommunications, 710121, China;2. MOEKLINNS Lab, Department of Computer Science and Technology, Xian Jiaotong University, 710049, China;1. College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China;2. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China;1. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;2. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. Zhejiang Provincial Key Lab of Data Storage and Transmission Technology, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China;1. School of Computer Science and Technology, Soochow University, Suzhou 215006, PR China;2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;3. Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong |
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| Abstract: | 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. |
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| Keywords: | Extreme learning machine Reduced kernel extreme learning machine Activity recognition Support vector machine |
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