Deep Bidirectional GRU network for human activity recognition using Wearable inertial sensors

Human activity recognition (HAR) has a wide range of applications in medical care, elderly care and so on. Due to the development of wearable sensor technology, HAR based on wearable sensors has gradually become a hot research topic. In terms of feature extraction of raw data, traditional methods usually use manually extracted features, which has many limitations. The method based on deep learning has gradually become the mainstream. In this paper, a deep bidirectional GRU network for human activity recognition is proposed, which uses few data preprocessing to classify and recognize daily activities. An accuracy rate of 95.69% is achieved on the UCL-HAR dataset which is an increase of 2.8% compared to the bidirectional LSTM network. In addition, the proposed method in this paper reduces the training time by 11.8 %, which will help the neural network to be deployed in real-time applications.