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LSTM for diagnosis of neurodegenerative diseases using gait data
Author(s): Aite Zhao; Lin Qi; Jie Li; Junyu Dong; Hui Yu
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Paper Abstract

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

Paper Details

Date Published: 10 April 2018
PDF: 9 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106155B (10 April 2018); doi: 10.1117/12.2305277
Show Author Affiliations
Aite Zhao, Ocean Univ. of China (China)
Lin Qi, Ocean Univ. of China (China)
Jie Li, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)
Hui Yu, Univ. of Portsmouth (United Kingdom)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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