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

Nuno Bento, Joana Rebelo, Marília Barandas, André V. Carreiro, Andrea Campagner, Federico Cabitza, Hugo Gamboa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Article number7324
Number of pages20
JournalSensors
Volume22
Issue number19
DOIs
Publication statusPublished - 27 Sep 2022

Keywords

  • accelerometer
  • deep learning
  • domain generalization
  • human activity recognition

Fingerprint

Dive into the research topics of 'Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition'. Together they form a unique fingerprint.

Cite this