Cargando…
Empirical Mode Decomposition Based Multi-Modal Activity Recognition
This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. H...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662633/ https://www.ncbi.nlm.nih.gov/pubmed/33114352 http://dx.doi.org/10.3390/s20216055 |
_version_ | 1783609441727283200 |
---|---|
author | Hu, Lingyue Zhao, Kailong Zhou, Xueling Ling, Bingo Wing-Kuen Liao, Guozhao |
author_facet | Hu, Lingyue Zhao, Kailong Zhou, Xueling Ling, Bingo Wing-Kuen Liao, Guozhao |
author_sort | Hu, Lingyue |
collection | PubMed |
description | This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition. |
format | Online Article Text |
id | pubmed-7662633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76626332020-11-14 Empirical Mode Decomposition Based Multi-Modal Activity Recognition Hu, Lingyue Zhao, Kailong Zhou, Xueling Ling, Bingo Wing-Kuen Liao, Guozhao Sensors (Basel) Letter This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition. MDPI 2020-10-24 /pmc/articles/PMC7662633/ /pubmed/33114352 http://dx.doi.org/10.3390/s20216055 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Letter Hu, Lingyue Zhao, Kailong Zhou, Xueling Ling, Bingo Wing-Kuen Liao, Guozhao Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title_full | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title_fullStr | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title_full_unstemmed | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title_short | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
title_sort | empirical mode decomposition based multi-modal activity recognition |
topic | Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662633/ https://www.ncbi.nlm.nih.gov/pubmed/33114352 http://dx.doi.org/10.3390/s20216055 |
work_keys_str_mv | AT hulingyue empiricalmodedecompositionbasedmultimodalactivityrecognition AT zhaokailong empiricalmodedecompositionbasedmultimodalactivityrecognition AT zhouxueling empiricalmodedecompositionbasedmultimodalactivityrecognition AT lingbingowingkuen empiricalmodedecompositionbasedmultimodalactivityrecognition AT liaoguozhao empiricalmodedecompositionbasedmultimodalactivityrecognition |