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Sensors (Basel). 2016 Dec 2;16(12). pii: E2048.

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

Xu H1,2, Liu J3,4, Hu H5,6, Zhang Y7.

Author information

1
Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China. hl-xu16@mails.tsinghua.edu.cn.
2
Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China. hl-xu16@mails.tsinghua.edu.cn.
3
Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China. liujinyi16@mails.ucas.ac.cn.
4
School of Computer and Control, University of Chinese Academy of Sciences, Beijing 100190, China. liujinyi16@mails.ucas.ac.cn.
5
Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China. haibo.hu@cqu.edu.cn.
6
School of Software Engineering, Chongqing University, Chongqing 401331, China. haibo.hu@cqu.edu.cn.
7
Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China. zhyi@tsinghua.edu.cn.

Abstract

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.

KEYWORDS:

Hilbert-Huang transform; activity recognition; feature extraction; wearable sensors

PMID:
27918414
PMCID:
PMC5191029
DOI:
10.3390/s16122048
[Indexed for MEDLINE]
Free PMC Article

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