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
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human Action Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample measures of a body’s tangential acceleration and speed using inertial sensors and train them to learn model skills of correctly categorising behaviour into relevant categories. In detecting human activities, the use of detectors in personal and portable devices has increased to understand better and anticipate human behaviour. Many specialists are working toward developing a classification that can distinguish between a user’s behaviour and raw data while utilising as few resources 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