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LSTM for diagnosis of neurodegenerative diseases using gait data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Neurodegenerative diseases (NDs) usually cause gait disorders and postural disorders, which provides an important basis for NDs diagnosis. By observing and analyzing these clinical manifestations, medical specialists finally give diagnostic results to the patient, which is inefficient and can be easily affected by doctors' subjectivity. In this paper, we propose a two-layer Long Short-Term Memory (LSTM) model to learn the gait patterns exhibited in the three NDs. The model was trained and tested using temporal data that was recorded by force-sensitive resistors including time series, such as stride interval and swing interval. Our proposed method outperforms other methods in literature in accordance with accuracy of the predicted diagnostic result. Our approach aims at providing the quantitative assessment so that to indicate the diagnosis and treatment of these neurodegenerative diseases in clinic.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Graphics and Image Processing
EditorsHui Yu, Junyu Dong
PublisherSPIE Press
ISBN (Electronic)9781510617421
ISBN (Print)9781510617414
DOIs
Publication statusPublished - 10 Apr 2018
Event9th International Conference on Graphic and Image Processing: ICGIP 2017 - Qingdao, China
Duration: 14 Oct 201716 Oct 2017
http://www.icgip.org/

Publication series

NameProceedings of SPIE
PublisherSociety of Photo-Optical Instrumentation Engineers
Volume10615
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference9th International Conference on Graphic and Image Processing
CountryChina
CityQingdao
Period14/10/1716/10/17
Internet address

Documents

  • LSTM for Diagnosis of Neurodegenerative Diseases_ICGIP2017

    Rights statement: A. Zhao, L. Qi, J. Dong & H. Yu. 'LSTM for diagnosis of neurodegenerative diseases using gait data'. Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106155B (2018). DOI: 10.1117/12.2305277. Copyright © 2018, Society of Photo-Optical Instrumentation Engineers. All rights reserved.

    Accepted author manuscript (Post-print), 513 KB, PDF-document

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