Paper Title
Machine Learning Based Human Activity Detection

Abstract
Human Activity Recognition (HAR) aims to identifiy human activities based on sensor estimations, as well as to recognise accurate and efficient human behaviour represents as a challenging field of research in computer vision. To overcome the challenges, the two basic models: Convolutional Neural Network (CNN) and Deep Learning Long Short-Term Memory (LSTM) had the accurate results for all users, with 91.77% and 92.43%, respectively. Convolutional Neural Networks (CNNs) have emerged as a useful category of systems for issues involving image recognition or computer vision. We investigate several methods for increasing a CNN's connection in the time domain to take benefits from local spatiotemporal data, and recommend a multi-resolution, foveal structure as a potential method to quicken training. We propose an experimental and improved approach that combines improved hand-crafted features with neural network architecture that outperform powerful methods while applying the same standardized score to different datasets. Finally, we offer a variety of analysis- related suggestions for researchers. This survey report is a valuable resource for people interested in future research on human activity recognition. Keywords - Convolution Neural Network, Deep Learning, Human Activity Detection, Accelerometer Data, Long-Short Term Memory, Wireless Sensor Data Mining

Author - Mudit Saxena, Kashish Tiwari, Keertika Singh,Vivek Kumar

Published : Volume-10,Issue-7  ( Jul, 2023 )

	|		PDF	|	Viewed - 11	|	Published on 2023-11-15