A Close Look into Human Activity Recognition Models using Deep Learning
This is an incremental survey paper for researchers in human activity recognition.
This paper surveys state-of-the-art deep learning models for human activity recognition, analyzing their implementation strategies and potential limitations.
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.