Discrimination between Upstairs and Downstairs Based on Accelerometer

Yang XUE; Lianwen JIN

doi:10.1587/transinf.E94.D.1173

Discrimination between Upstairs and Downstairs Based on Accelerometer

Yang XUE, Lianwen JIN

Full Text Views

0

Share
Cite this

Summary :

An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.

Publication: IEICE TRANSACTIONS on Information Vol.E94-D No.6 pp.1173-1177

Publication Date: 2011/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E94.D.1173

Type of Manuscript: Special Section LETTER (Special Section on Emerging Technologies of Ubiquitous Computing Systems)

Category

Cite this

Copy

Yang XUE, Lianwen JIN, "Discrimination between Upstairs and Downstairs Based on Accelerometer" in IEICE TRANSACTIONS on Information, vol. E94-D, no. 6, pp. 1173-1177, June 2011, doi: 10.1587/transinf.E94.D.1173.
Abstract: An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1173/_p

Copy

@ARTICLE{e94-d_6_1173,
author={Yang XUE, Lianwen JIN, },
journal={IEICE TRANSACTIONS on Information},
title={Discrimination between Upstairs and Downstairs Based on Accelerometer},
year={2011},
volume={E94-D},
number={6},
pages={1173-1177},
abstract={An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transinf.E94.D.1173},
ISSN={1745-1361},
month={June},}

Copy

TY - JOUR
TI - Discrimination between Upstairs and Downstairs Based on Accelerometer
T2 - IEICE TRANSACTIONS on Information
SP - 1173
EP - 1177
AU - Yang XUE
AU - Lianwen JIN
PY - 2011
DO - 10.1587/transinf.E94.D.1173
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2011
AB - An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.
ER -