IJEEDC Human Activity Detection and Classification using Machine Learning

Journal Paper

Paper Title :Human Activity Detection and Classification using Machine Learning

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

Article Citation :Mudit Saxena ,Keertika Singh ,Kashish Tiwari ,Vivek Kumar , (2023 ) " Human Activity Detection and Classification using Machine Learning " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 59-67, Volume-11,Issue-6

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.

Type : Research paper

Published : Volume-11,Issue-6


	\|		PDF	\|	Viewed - 29	\|	Published on 2023-10-19

Jan. 2025
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
Issue Published	:	141
Paper Published	:	1771
No. of Authors	:	4924

Published : Volume-11,Issue-6

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