Human Action Recognition using Long Short-Term Memory and Convolutional Neural Network Model
Shreyas Pagare1, Rakesh Kumar2

1Shreyas Pagare, Research Scholar, Department of Computer Science & Engineering, RNTU University, Bhopal (M.P), India.

2Dr. Rakesh Kumar, Research Guide, Department of Computer Science & Engineering, RNTU University, Bhopal (M.P), India.

Manuscript received on 13 July 2023 | Revised Manuscript received on 10 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 June 2024 | PP: 20-26 | Volume-14 Issue-2, May 2024 | Retrieval Number: 100.1/ijsce.I96970812923 | DOI: 10.35940/ijsce.I9697.14020524

Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Human Action Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample some measures of a body’s tangential acceleration and speed using inertial sensors and exercise them only to learn model skills of incorrectly categorizing behavior into the relevant categories. In detecting human activities, the use of detectors in personal and portable devices has increased to better understand and anticipate human behavior. Many specialists are working toward developing a classification that can distinguish between a user’s behavior and uncooked data while utilizing as few reserves as possible. A Long-term Recurrent Convolutional Network (LRCN) is proposed as a comprehensive human action recognition system based on deep neural networks in this paper. 

Keywords: Human Action Recognition, Convolutional Neural Network, Long Short-Term Memory, Long Short-Term Memory
Scope of the Article: Convolutional Neural Network