A Close Look into Human Activity Recognition Models using Deep Learning

• URL: http://arxiv.org/abs/2204.13589v1
• Date: Tue, 26 Apr 2022 19:43:21 GMT
• Title: A Close Look into Human Activity Recognition Models using Deep Learning
• Authors: Wei Zhong Tee, Rushit Dave, Naeem Seliya, Mounika Vanamala
• Abstract summary: This paper surveys some state-of-the-art human activity recognition models based on deep learning architecture. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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