Cargando…

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Huile, Liu, Jinyi, Hu, Haibo, Zhang, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191029/
https://www.ncbi.nlm.nih.gov/pubmed/27918414
http://dx.doi.org/10.3390/s16122048
_version_ 1782487539346571264
author Xu, Huile
Liu, Jinyi
Hu, Haibo
Zhang, Yi
author_facet Xu, Huile
Liu, Jinyi
Hu, Haibo
Zhang, Yi
author_sort Xu, Huile
collection PubMed
description Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.
format Online
Article
Text
id pubmed-5191029
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-51910292017-01-03 Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform Xu, Huile Liu, Jinyi Hu, Haibo Zhang, Yi Sensors (Basel) Article Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. MDPI 2016-12-02 /pmc/articles/PMC5191029/ /pubmed/27918414 http://dx.doi.org/10.3390/s16122048 Text en © 2016 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 Article
Xu, Huile
Liu, Jinyi
Hu, Haibo
Zhang, Yi
Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title_full Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title_fullStr Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title_full_unstemmed Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title_short Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
title_sort wearable sensor-based human activity recognition method with multi-features extracted from hilbert-huang transform
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191029/
https://www.ncbi.nlm.nih.gov/pubmed/27918414
http://dx.doi.org/10.3390/s16122048
work_keys_str_mv AT xuhuile wearablesensorbasedhumanactivityrecognitionmethodwithmultifeaturesextractedfromhilberthuangtransform
AT liujinyi wearablesensorbasedhumanactivityrecognitionmethodwithmultifeaturesextractedfromhilberthuangtransform
AT huhaibo wearablesensorbasedhumanactivityrecognitionmethodwithmultifeaturesextractedfromhilberthuangtransform
AT zhangyi wearablesensorbasedhumanactivityrecognitionmethodwithmultifeaturesextractedfromhilberthuangtransform