DOIONLINE 
DOIONLINE Publish In |
International Journal of Electrical, Electronics and Data Communication (IJEEDC)-IJEEDC |
 Journal Home Volume Issue |
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Issue |
Volume-11,Issue-6 ( Jun, 2023 ) | |||||||||
Paper Title |
Human Activity Detection and Classification using Machine Learning | |||||||||
Author Name |
Mudit Saxena, Keertika Singh, Kashish Tiwari, Vivek Kumar | |||||||||
Affilition |
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Pages |
59-67 | |||||||||
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 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. | |||||||||
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