Enhanced bag-of-words representation for human activity recognition using mobile sensor data

Bhuiyan, RA; Tarek, S; Tian, H

Enhanced bag-of-words representation for human activity recognition using mobile sensor data

Bhuiyan, RA Tarek, S Tian, H

Permalink

Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Signal, Image and Video Processing, 2021, 15, (8), pp. 1739-1746
Issue Date:: 2021-11-01

Closed Access

	Filename	Description	Size
	Bhuiyan2021_Article_EnhancedBag-of-wordsRepresenta.pdf	Published version	1.09 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Bhuiyan, RA
dc.contributor.author	Tarek, S
dc.contributor.author	Tian, H https://orcid.org/0000-0002-2889-6158
dc.date.accessioned	2022-04-23T20:52:39Z
dc.date.available	2022-04-23T20:52:39Z
dc.date.issued	2021-11-01
dc.identifier.citation	Signal, Image and Video Processing, 2021, 15, (8), pp. 1739-1746
dc.identifier.issn	1863-1703
dc.identifier.issn	1863-1711
dc.identifier.uri	http://hdl.handle.net/10453/156560
dc.description.abstract	Human activity recognition based on sensors (e.g., accelerometer and gyroscope) embedded in smartphones is of great significance for many applications under uncontrolled environments. Although significant progress has been noticed in this field, one of the challenges limiting its real-life applications lies in robust feature extraction for efficient activity recognition on smartphones. This study addresses this challenge by proposing an improved bag-of-words representation for activity signal characterization. Specifically, raw activity signals are processed by discrete wavelet transformation to extract local features, which will be clustered using K-means to form a bag-of-words dictionary. The vocabularies in the dictionary are regarded as bin centers for histogram feature construction. For each local feature of an activity signal, its distance from all the bin centers will be measured. To capture higher-order information for feature representation, the frequency for the bin centers corresponding to the minimum n distances will be updated. Moreover, the frequency is increased by a trigonometry constraint cosine value of the corresponding distances to account for activity signals’ structural information. The proposed feature representation has been verified with three well-established classifiers, namely SVM, ANN, and KNN on the UCI-HAR dataset. The consistently good performance validates the effectiveness and robustness of the proposed feature representation. Compared with the state-of-the-art, the experimental results also demonstrate the advantage of the proposed method in terms of accuracy and computational cost.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation.ispartof	Signal, Image and Video Processing
dc.relation.isbasedon	10.1007/s11760-021-01907-4
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Enhanced bag-of-words representation for human activity recognition using mobile sensor data
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-23T20:52:37Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	8

Abstract:

Human activity recognition based on sensors (e.g., accelerometer and gyroscope) embedded in smartphones is of great significance for many applications under uncontrolled environments. Although significant progress has been noticed in this field, one of the challenges limiting its real-life applications lies in robust feature extraction for efficient activity recognition on smartphones. This study addresses this challenge by proposing an improved bag-of-words representation for activity signal characterization. Specifically, raw activity signals are processed by discrete wavelet transformation to extract local features, which will be clustered using K-means to form a bag-of-words dictionary. The vocabularies in the dictionary are regarded as bin centers for histogram feature construction. For each local feature of an activity signal, its distance from all the bin centers will be measured. To capture higher-order information for feature representation, the frequency for the bin centers corresponding to the minimum n distances will be updated. Moreover, the frequency is increased by a trigonometry constraint cosine value of the corresponding distances to account for activity signals’ structural information. The proposed feature representation has been verified with three well-established classifiers, namely SVM, ANN, and KNN on the UCI-HAR dataset. The consistently good performance validates the effectiveness and robustness of the proposed feature representation. Compared with the state-of-the-art, the experimental results also demonstrate the advantage of the proposed method in terms of accuracy and computational cost.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/156560