Skip to content
Back to outputs

LSTM for diagnosis of neurodegenerative diseases using gait data

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

Standard

LSTM for diagnosis of neurodegenerative diseases using gait data. / Zhao, Aite; Qi, Lin; Li, Jie; Dong, Junyu; Yu, Hui.

Proceedings of the 9th International Conference on Graphics and Image Processing. ed. / Hui Yu; Junyu Dong. SPIE Press, 2018. 106155B (Proceedings of SPIE; Vol. 10615).

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

Harvard

Zhao, A, Qi, L, Li, J, Dong, J & Yu, H 2018, LSTM for diagnosis of neurodegenerative diseases using gait data. in H Yu & J Dong (eds), Proceedings of the 9th International Conference on Graphics and Image Processing., 106155B, Proceedings of SPIE, vol. 10615, SPIE Press, 9th International Conference on Graphic and Image Processing, Qingdao, China, 14/10/17. https://doi.org/10.1117/12.2305277

APA

Zhao, A., Qi, L., Li, J., Dong, J., & Yu, H. (2018). LSTM for diagnosis of neurodegenerative diseases using gait data. In H. Yu, & J. Dong (Eds.), Proceedings of the 9th International Conference on Graphics and Image Processing [106155B] (Proceedings of SPIE; Vol. 10615). SPIE Press. https://doi.org/10.1117/12.2305277

Vancouver

Zhao A, Qi L, Li J, Dong J, Yu H. LSTM for diagnosis of neurodegenerative diseases using gait data. In Yu H, Dong J, editors, Proceedings of the 9th International Conference on Graphics and Image Processing. SPIE Press. 2018. 106155B. (Proceedings of SPIE). https://doi.org/10.1117/12.2305277

Author

Zhao, Aite ; Qi, Lin ; Li, Jie ; Dong, Junyu ; Yu, Hui. / LSTM for diagnosis of neurodegenerative diseases using gait data. Proceedings of the 9th International Conference on Graphics and Image Processing. editor / Hui Yu ; Junyu Dong. SPIE Press, 2018. (Proceedings of SPIE).

Bibtex

@inproceedings{1642770e5b6741dd8ef5c2c6a9187406,
title = "LSTM for diagnosis of neurodegenerative diseases using gait data",
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.",
author = "Aite Zhao and Lin Qi and Jie Li and Junyu Dong and Hui Yu",
note = "No embargo period.; 9th International Conference on Graphic and Image Processing : ICGIP 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2018",
month = apr,
day = "10",
doi = "10.1117/12.2305277",
language = "English",
isbn = "9781510617414",
series = "Proceedings of SPIE",
publisher = "SPIE Press",
editor = "Hui Yu and Junyu Dong",
booktitle = "Proceedings of the 9th International Conference on Graphics and Image Processing",
url = "http://www.icgip.org/",

}

RIS

TY - GEN

T1 - LSTM for diagnosis of neurodegenerative diseases using gait data

AU - Zhao, Aite

AU - Qi, Lin

AU - Li, Jie

AU - Dong, Junyu

AU - Yu, Hui

N1 - No embargo period.

PY - 2018/4/10

Y1 - 2018/4/10

N2 - 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.

AB - 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.

UR - https://spie.org/publications/contact-spie-publications/reprint-permission?SSO=1

U2 - 10.1117/12.2305277

DO - 10.1117/12.2305277

M3 - Conference contribution

SN - 9781510617414

T3 - Proceedings of SPIE

BT - Proceedings of the 9th International Conference on Graphics and Image Processing

A2 - Yu, Hui

A2 - Dong, Junyu

PB - SPIE Press

T2 - 9th International Conference on Graphic and Image Processing

Y2 - 14 October 2017 through 16 October 2017

ER -

ID: 7993894