Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices.