Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world envir...

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Bibliographic Details
Main Authors: Nuno Bento, Joana Rebelo, Marília Barandas, André V. Carreiro, Andrea Campagner, Federico Cabitza, Hugo Gamboa
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
human activity recognition
deep learning
domain generalization
accelerometer
Online Access:https://www.mdpi.com/1424-8220/22/19/7324

Internet

https://www.mdpi.com/1424-8220/22/19/7324

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