Details
| Original language | English |
|---|---|
| Title of host publication | 2017 22nd International Conference on Digital Signal Processing (DSP) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 4 |
| ISBN (electronic) | 9781538618950 |
| ISBN (print) | 9781538618967 |
| Publication status | Published - 7 Nov 2017 |
| Event | 2017 22nd International Conference on Digital Signal Processing, DSP 2017 - London, United Kingdom (UK) Duration: 23 Aug 2017 → 25 Aug 2017 |
Publication series
| Name | International Conference on Digital Signal Processing (DSP) |
|---|---|
| ISSN (electronic) | 2165-3577 |
Abstract
To foster diagnosis of gait abnormalities as well as tracking the recovery rate in the course of healing, automated gait classification methods have great added value. Therefore, gait classification based on a dictionary learning approach was developed and tested. With an average classification rate of about 93%, the proposed method offers great potential to be deployed in support of digital healthcare and therapy. Moreover, by providing efficient data storage as well as low runtime, it is ideal for use in portable diagnostic tools.
Keywords
- dictionary learning, gait classification, Sparse coding
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
Cite this
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2017 22nd International Conference on Digital Signal Processing (DSP). Institute of Electrical and Electronics Engineers Inc., 2017. (International Conference on Digital Signal Processing (DSP)).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Dictionary Learning Based Approach for Gait Classification
AU - Poschadel, Nils
AU - Moghaddamnia, Sanam
AU - Alcaraz, Javier Conte
AU - Steinbach, Marc
AU - Peissig, Jürgen
N1 - Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/11/7
Y1 - 2017/11/7
N2 - To foster diagnosis of gait abnormalities as well as tracking the recovery rate in the course of healing, automated gait classification methods have great added value. Therefore, gait classification based on a dictionary learning approach was developed and tested. With an average classification rate of about 93%, the proposed method offers great potential to be deployed in support of digital healthcare and therapy. Moreover, by providing efficient data storage as well as low runtime, it is ideal for use in portable diagnostic tools.
AB - To foster diagnosis of gait abnormalities as well as tracking the recovery rate in the course of healing, automated gait classification methods have great added value. Therefore, gait classification based on a dictionary learning approach was developed and tested. With an average classification rate of about 93%, the proposed method offers great potential to be deployed in support of digital healthcare and therapy. Moreover, by providing efficient data storage as well as low runtime, it is ideal for use in portable diagnostic tools.
KW - dictionary learning
KW - gait classification
KW - Sparse coding
UR - http://www.scopus.com/inward/record.url?scp=85040368835&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2017.8096121
DO - 10.1109/ICDSP.2017.8096121
M3 - Conference contribution
AN - SCOPUS:85040368835
SN - 9781538618967
T3 - International Conference on Digital Signal Processing (DSP)
BT - 2017 22nd International Conference on Digital Signal Processing (DSP)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 22nd International Conference on Digital Signal Processing, DSP 2017
Y2 - 23 August 2017 through 25 August 2017
ER -