eprintid: 3054
rev_number: 7
eprint_status: archive
userid: 12
dir: disk0/00/00/30/54
datestamp: 2018-09-21 04:20:50
lastmod: 2018-09-21 04:20:50
status_changed: 2018-09-21 04:20:50
type: article
metadata_visibility: show
creators_name: Pham Van, Thanh
creators_name: Nguyen Duc, Anh
creators_name: Dang Nhu, Dinh
creators_name: Pham Hong, Hai
creators_name: Tran Van, An
creators_name: Sandrasegaran, Kumbesan
creators_name: Tran Duc, Tan
creators_id: phamvanthanh1209@gmail.com
creators_id: anhnd@gmail.com
creators_id: dangnhu@gmail.com
creators_id: honghaipham.bk@gmail.com
creators_id: antv79@gmail.com
creators_id: kumbesan.sandrasegaran@uts.edu.au
creators_id: tantd@vnu.edu.vn
title: Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System
ispublished: pub
subjects: ElectronicsandComputerEngineering
subjects: isi
divisions: fac_fet
abstract: Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).
date: 2018-09-20
date_type: published
publisher: MPDI
official_url: http://www.mdpi.com/journal/sensors
id_number: 10.3390/s18103186
full_text_status: public
publication: Sensors
volume: 18
number: 10
pagerange: 1-22
refereed: TRUE
issn: 1424-8220
related_url_url: http://www.mdpi.com/1424-8220/18/10/3186/htm
citation:   Pham Van, Thanh and Nguyen Duc, Anh and Dang Nhu, Dinh and Pham Hong, Hai and Tran Van, An and Sandrasegaran, Kumbesan and Tran Duc, Tan  (2018) Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System.  Sensors, 18  (10).   pp. 1-22.  ISSN 1424-8220     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3054/1/sensors-18-03186.pdf