Human activity recognition (HAR) is a main research field of context-aware computing;the performance of HAR mainly depends on the feature extraction method and classification algorithm. extremelearningmachine (ELM) ...
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Human activity recognition (HAR) is a main research field of context-aware computing;the performance of HAR mainly depends on the feature extraction method and classification algorithm. extremelearningmachine (ELM) is a single hidden layer neural network, which has better classification and generalization ability. However, ELM is not suitable for feature extraction. deeplearning is a hot research field as it can automatically extract significant features from raw data. In this paper, we propose an approach: an ELM-baseddeepmodel, which combined convolutional neural network (CNN), multilayer ELM (ML-ELM) as feature extractor, and used kernel ELM (KELM) as classifier. We used CNN and ML-ELM to extract significant features, and used KELM to achieve stable performance. The performance of proposed approach is validated on two public HAR datasets, and the experimental results show that the proposed approach is effective.
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