Parkinson's Disease Motor Symptoms in Machine Learning: A Review

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 30 Nov 2013Embargo end date: 01 Jan 2013Publisher:Academy and Industry Research Collaboration Center (AIRCC)Journal:Health Informatics - An International Journal, volume 2, pages 1-18 (issn: 2319-3190, eissn: 2319-2046,

Copyright policy )Funded by:EC | REMPARK

Authors: Claas Ahlrichs; Michael Lawo;

doi: 10.5121/hiij.2013.2401 , 10.48550/arxiv.1312.3825 , 10.5281/zenodo.3938423 , 10.5281/zenodo.1240690 , 10.5281/zenodo.1240689 , 10.5281/zenodo.3938424

arXiv: 1312.3825

Parkinson's Disease Motor Symptoms in Machine Learning: A Review

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Abstract

This paper reviews related work and state-of-the-art publications for recognizing motor symptoms of Parkinson's Disease (PD). It presents research efforts that were undertaken to inform on how well traditional machine learning algorithms can handle this task. In particular, four PD related motor symptoms are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia) and their details summarized. Thus the primary objective of this research is to provide a literary foundation for development and improvement of algorithms for detecting PD related motor symptoms.

Health Informatics: An International Journal (HIIJ), November 2013, Volume 2, Number 4

Related Organizations

University of Bremen
Germany

Keywords

Machine Learning, FOS: Computer and information sciences, Parkinson's Disease, Artificial Intelligence (cs.AI), Artificial Intelligenc, Computer Science - Artificial Intelligence, Review, State-of-the-Art, Parkinson's Disease, Machine Learning, Artificial Intelligence, Review, State-of-the-Art

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