School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China.
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China.
Sensors (Basel). 2020 Mar 30;20(7):1933. doi: 10.3390/s20071933.
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 are improved by at least 1.542%, 7.79% and 1.69%, respectively.
日常活动预测在智能家居居民的日常生活中起着重要作用。日常活动的类别预测和发生时间预测是两项关键任务。日常活动的类别预测与发生时间预测相关，但现有研究仅关注这两个任务之一。此外，当这两个任务按顺序执行时，日常活动预测的性能较低。本文提出了一种基于多任务学习的预测模型，用于相互迭代地预测日常活动的类别和发生时间。首先，对原始传感器事件进行预处理，形成日常活动的特征空间。其次，开发了一个结合卷积神经网络（CNN）和双向长短时记忆（Bi-LSTM）单元的并行多任务学习模型作为预测模型。最后，使用五个不同的数据集来评估所提出的模型。实验结果表明，与最先进的单任务学习模型相比，该模型的准确率至少提高了 2.22%，NMAE、NRMSE 和 R 的指标至少提高了 1.542%、7.79%和 1.69%。
