Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition.
Sensors (Basel)
; 22(19)2022 Sep 27.
Article
em En
| MEDLINE
| ID: mdl-36236427
ABSTRACT
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 environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Atividades Humanas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Portugal
País de publicação:
CH
/
SUIZA
/
SUÍÇA
/
SWITZERLAND