Resumen
Daily activity recognition between different smart home environments faces some challenges, such as an insufficient amount of data and differences in data distribution. However, a deep network requires a large amount of labeled data for training. Additionally, inconsistent data distribution can lead to over-fitting of network learning. Additionally, the time cost of training the network from scratch is too high. In order to solve the above problems, this paper proposes a fine-tuning method suitable for daily activity recognition, which is the first application of this method in our field. Firstly, we unify the sensor space and activity space to reduce the variability in heterogeneous environments. Then, the Word2Vec algorithm is used to transform the activity samples into digital vectors recognizable by the network. Finally, the deep network is fine-tuned to transfer knowledge and complete the recognition task. Additionally, we try to train the network on public datasets. The results show that the network trained on a small dataset also has good transferability. It effectively improves the recognition accuracy and reduces the time cost and heavy data annotation.