Human Activity Detection and Classification using Machine Learning
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 multiresolution,
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.