Abstract-A lot of real-life mobile sensing applications are becoming available. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. In this paper, we propose a LSTM-based feature extraction approach to recognize human activities using tri-axial accelerometers data. The experimental results on the (WISDM) Lab public datasets indicate that our LSTM-based approach is practical and achieves 92.1% accuracy.
Chen REF proposed a feature extraction method based on long short-term memory neural network to recognize the human activities with accelerometer data.
LSTM Networks for Mobile Human Activity Recognition
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Computer Science