Analysis of Human Activity Recognition using Deep Learning

摘要

Nowadays the deluge of data is increasing with new technologies coming up daily. These advancements in recent times have also led to an increased growth in fields like Robotics and Internet of Things (IoT). This paper helps us draw a comparison between the usage and accuracy of different Human Activity Recognition models. There will be discussion on mainly two models-2-D Convolutional Neural Network and Long-Short term Memory. In order to maintain the consistency and credibility of the survey, both models are trained using the same dataset containing information collected using wearable sensors which was acquired from a public website. They are compared using their accuracy and confusion matrix to check the true and false positives and later the various aspects and fields, where the two models can separately and together be used in the wider field of Human Activity Recognition using image data have been explained. The experimental results signified that both Convolutional Neural Networks and Long-Short term memory model are equally equipped for different situations, yet Long-Short Term memory model mostly appears to be more consistent than Convolutional Neural Networks.