An Ensemble of Autonomous Auto-Encoders for Human Activity Recognition

Kemilly Dearo Garcia*, Claudio Frederico Pinho Rebelo de Sá, Mannes Poel, Thiago Carvalho, João Mendes-Moreira, João M.P. Cardoso, André C.P.L.F. de Carvalho, Joost Nico Kok

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

21 Downloads (Pure)

Abstract

Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model.
Original languageEnglish
JournalNeurocomputing
DOIs
Publication statusE-pub ahead of print/First online - 26 Jan 2021

Keywords

  • UT-Hybrid-D
  • Ensemble of Auto-Encoders
  • Semi-supervised learning
  • Human activity recognition

Fingerprint Dive into the research topics of 'An Ensemble of Autonomous Auto-Encoders for Human Activity Recognition'. Together they form a unique fingerprint.

Cite this