SVM Time Series Classification of Selected Gait Abnormalities-Reference-Cited by-同舟云学术

SVM Time Series Classification of Selected Gait Abnormalities

Published:2022 Issue: Volume: Page:195-209
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Rostovski Jakob^ORCID,Krivošei Andrei^ORCID,Kuusik Alar^ORCID,Ahmadov Ulvi^ORCID,Alam Muhammad Mahtab^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95593-9_16

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3. di Biase, L., et al.: Gait analysis in Parkinson’s disease: an overview of the most accurate markers for diagnosis and symptoms monitoring. Sensors 20(12) (2020). https://doi.org/10.3390/s20123529. https://www.mdpi.com/1424-8220/20/12/3529

4. Buongiorno, D., Bortone, I., Cascarano, G.D., Trotta, G.F., Brunetti, A., Bevilacqua, V.: A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s disease. BMC Med. Inform. Decis. Mak. 19, 1–13 (2019)

5. Camps, J., et al.: Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit. Knowl.-Based Syst. 139, 119–131 (2018). https://doi.org/10.1016/j.knosys.2017.10.017

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