AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design

AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design

Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of time- series data when used for Human Activity Recognition (HAR). The manual design of such neural architectures is an error-prone and time-consuming process. The search for optimal CNN architectures is con...

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Bibliographic Details
Main Authors: Walaa N. Ismail, Hessah A. Alsalamah, Mohammad Mehedi Hassan, Ebtesam Mohamed
Format: Article
Language:English
Published: Elsevier 2023-02-01
Series:Heliyon
Subjects:
Human activity recognition
Deep learning
Convolution neural networks
CNN topology
Genetic algorithms
Evolutionary neural network search
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844023008435

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http://www.sciencedirect.com/science/article/pii/S2405844023008435

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