Volume 1 Issue 4 | 2024 | View PDF
Paper Id:IJMSM-V1I4P106
doi: 10.71141/30485037/V1I4P106

Human Activity Recognition (HAR) System Using Deep Learning

Stephen Mkegh Nengem, Friday Haruna, Samuel Ayua

Citation:
Stephen Mkegh Nengem, Friday Haruna, Samuel Ayua, "Human Activity Recognition (HAR) System Using Deep Learning" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 4, pp. 31-41, 2024.

Abstract:
This research explores the application of deep learning in human activity recognition (HAR). As technological advancements continue, HAR plays a pivotal role in fields like healthcare, sports, and security. Leveraging deep learning models, particularly neural networks (NN) and convolutional neural networks (CNN), enhances the accuracy and efficiency of HAR systems. Hence, the article discretized the outline of the human constitution into various limited variables. At that point entire solidness network of the outline was determined with the guide of the use of a developed and trained human activity recognition model; Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and combined CNN-LSTM models. The frames were compared and predict the type of activity, classification and captioning were done in real time. The analysis of their performances were compared to get the optimal result.

Keywords: Human Activity Recognition (HAR), Convolution Neural Networks (CNN) Deep learning, Human Computer Interaction.

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