Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters-Reference-Cited by-同舟云学术

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Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters

Published:2021-01-28 Issue:2 Volume:128 Page:181-189
ISSN:0300-9564
Container-title:Journal of Neural Transmission
language:en
Short-container-title:J Neural Transm

Author:

Park Dong Jun,Lee Jun Woo,Lee Myung Jun,Ahn Se Jin,Kim Jiyoung,Kim Gyu Lee,Ra Young Jin,Cho Yu Na,Jeong Weui Bong

Funder

National Research Foundation of Korea

Pusan National University Hospital

Publisher

Springer Science and Business Media LLC

Subject

Biological Psychiatry,Psychiatry and Mental health,Clinical Neurology,Neurology

Link

http://link.springer.com/content/pdf/10.1007/s00702-021-02301-7.pdf

Reference37 articles.

1. Alaskar H, Hussain A (2018) Prediction of Parkinson disease using gait signals. 11th international conference on developments in esystems engineering (DeSE):23–26. https://doi.org/10.1109/DeSE.2018.00011

2. Albani G, Ferraris C, Nerino R, Chimienti A, Pettiti G, Parisi F, Priano L (2019) An Integrated multi-sensor approach for the remote monitoring of Parkinson’s disease. Sensors 19(21):4764. https://doi.org/10.3390/s19214764

3. Bajaj NP, Gontu V, Birchall J, Patterson J, Grosset DG, Lees AJ (2010) Accuracy of clinical diagnosis in tremulous parkinsonian patients: a blinded video study. J Neurol Neurosurg Psychiatry 81(11):1223–1228. https://doi.org/10.1136/jnnp.2009.193391

4. Bank PJ, Marinus J, Meskers CG, de Groot JH, van Hilten JJ (2017) Optical hand tracking: a novel technique for the assessment of bradykinesia in Parkinson’s disease. Mov Disord Pract 4(6):875–883. https://doi.org/10.1002/mdc3.12536

5. Bologna M, Paparella G, Fasano A, Hallett M, Berardelli A (2020a) Evolving concepts on bradykinesia. Brain 143(3):727–750. https://doi.org/10.1093/brain/awz344

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