  Human activity recognition using deep learning techniques has become
increasing popular because of its high effectivity with recognizing complex
tasks, as well as being relatively low in costs compared to more traditional
machine learning techniques. This paper surveys some state-of-the-art human
activity recognition models that are based on deep learning architecture and
has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory
(LSTM), or a mix of more than one type for a hybrid system. The analysis
outlines how the models are implemented to maximize its effectivity and some of
the potential limitations it faces.
