Published June 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Human Action Recognition using Long Short-Term Memory and Convolutional Neural Network Model

Creators

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

Contributors

Contact person:

Researcher:

  • 1. Research Scholar, Department of Computer Science & Engineering, RNTU University, Bhopal (M.P), India.
  • 2. Research Guide, Department of Computer Science & Engineering, RNTU University, Bhopal (M.P), India.

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijsce.I9697.14020524
EISSN
2231-2307

Dates

Accepted
2024-05-15
Manuscript received on 13 July 2023 | Revised Manuscript received on 10May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 June 2024.

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