A Survey on Different Deep Learning Model for Human Activity Recognition based on Application

Main Article Content

Hetal Bhaidasna, Chirag Patel, Zubin Bhaidasna


The field of human activity recognition (HAR) seeks to identify and classify an individual's unique movements or activities. However, recognizing human activity from video is a challenging task that requires careful attention to individuals, their behaviors, and relevant body parts. Multimodal activity recognition systems are necessary for many applications, including video surveillance systems, human-computer interfaces, and robots that analyze human behavior. This study provides a comprehensive analysis of recent breakthroughs in human activity classification, including different approaches, methodologies, applications, and limitations. Additionally, the study identifies several challenges that require further investigation and improvements. The specifications for an ideal human activity recognition dataset are also discussed, along with a thorough examination of the publicly available human activity classification datasets.

Article Details

How to Cite
Hetal Bhaidasna, et al. (2023). A Survey on Different Deep Learning Model for Human Activity Recognition based on Application. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 1496–1508. https://doi.org/10.17762/ijritcc.v11i10.8700
Author Biography

Hetal Bhaidasna, Chirag Patel, Zubin Bhaidasna

Hetal Bhaidasna1, Chirag Patel*2, Zubin Bhaidasna3

1Research Scholar, Computer Engineering Department,

Parul University

Vadodara, India


2Associate Professor , Computer Science &Engineering Department

CHANGA University

Kheda, India

Corresponding Author : chiragpatel.dce@charusat.ac.in

3Assistant Professor, Computer Engineering Department,

CVM University