期刊
SENSORS
卷 20, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/s20071933
关键词
multi-task learning; deep learning; daily activity forecast; smart home
资金
- National Natural Science Foundation of China [61976124, 61902050]
- Fundamental Research Funds for the Central Universities [3132019355]
- Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province [2018RYJ09, 2019RZJ01]
- CERNET Innovation Project [NGII20181203]
Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R-2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.
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