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Title	Hharnet: Taking Inspiration From Inception And Dense Networks For Human Activity Recognition Using Inertial Sensors
ID_Doc	28976
Authors	Imran H.A.; Latif U.
Year	2020
Published	HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI
DOI	http://dx.doi.org/10.1109/HONET50430.2020.9322655
Abstract	Human Activity Recognition (HAR) is an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human-computer interface, child care, education, and many more. The use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include an accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper, we have proposed a l-dimensional Convolutions Neural Network which is inspired by two state-of-the-art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, Fl-measure, and accuracies are reported. © 2020 IEEE.
Author Keywords	Activity Classification; Computer Vision Inspired lD-CNN; Convolutional Neural Network; Deep Neural Networks; Digital Signal Processing; Human Activity Recognition (HAR); Human Behavior Recognition; Inertial Sensors based classification

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