LSTM Networks for Mobile Human Activity Recognition

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.