Human activity recognition with self-attention

Abstrak

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.