Human activity recognition technology based on Transfer Learning is designed to solve the problem of lacking labeled training samples. In the traditional recognition model, the new sensor devices need a lot of labeled data to train its recognition model. The collection of these labeled data requires a lot of time and money. In this paper, we build a separate transfer recognition network model, which enables new sensor nodes to train with original sensor nodes without the need of re-collecting sample data. We notice that the state-of-the-art label-based transfer algorithm didn’t take into account the accuracy of label recognition. Therefore, we propose a Recognition-weighted kNN algorithm, and compare it with label transfer algorithm, and achieved good results.
CITATION STYLE
Tan, H., & Zhang, L. (2019). Separate human activity recognition model based on recognition-weighted kNN algorithm. In Lecture Notes in Electrical Engineering (Vol. 518, pp. 573–581). Springer Verlag. https://doi.org/10.1007/978-981-13-1328-8_74
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