Paper Title
HUMAN ACTIVITY DETECTION AND CLASSIFICATION USING MACHINE LEARNING

Abstract
Abstract - Human Activity Recognition (HAR) aims to identify human actions using 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 differentkey models: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) and Deep Learning Long Short-Term Memory (LSTM) had the accurate results for all users, with 71.77% and 72.43%, respectively. Convolutional Neural Networks (CNNs) and Recurrent Neural Network (RNNs) have emerged as a useful category of systems for issues involving image recognition or computer vision. We study many strategies for improving a CNN's time domain connections to benefit from locally spatio-temporal input, and we 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, Recurrent Neural Network, Deep Learning, Wireless Sensor Data Mining, Human Activity Detection, Accelerometer Data, Long-Short Term Memory.