A Survey on Deep Learning for Human Activity Recognition-Reference-Cited by-同舟云学术

A Survey on Deep Learning for Human Activity Recognition

Published:2022-11-30 Issue:8 Volume:54 Page:1-34
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Gu Fuqiang¹^ORCID,Chung Mu-Huan²,Chignell Mark²,Valaee Shahrokh²,Zhou Baoding³,Liu Xue⁴

Affiliation:

1. Chongqing University, Chongqing, China

2. University of Toronto, Toronto, ON, Canada

3. Shenzhen University, Shenzhen, Guangdong, China

4. McGill University, Montreal, Quebec, Canada

Abstract

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Guangdong Basic and Applied Basic Research Foundation

Shenzhen Scientific Research and Development Funding Program

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3472290

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