Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models-Reference-Cited by-同舟云学术

Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson’s Disease by Means of Machine Learning Models

Published:2023 Issue: Volume:31 Page:1056-1063
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Cesarelli Giuseppe¹^ORCID,Donisi Leandro¹,Amato Francesco²^ORCID,Romano Maria²^ORCID,Cesarelli Mario²,D'Addio Giovanni³,Ponsiglione Alfonso M.²^ORCID,Ricciardi Carlo²^ORCID

Affiliation:

1. Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Naples, Italy

2. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

3. Institute of Care and Scientific Research of Telese of ICS Maugeri SPA SB, Telese Terme, Italy

Funder

Call HUB Ricerca e Innovazione, Regione Lombardia and by Athics s.r.l.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10018500.pdf?arnumber=10018500

Reference75 articles.

1. A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease

2. Kinematics patterns of upper arm reaching movement in robot-mediated therapy

3. The biomechanics and motor control of gait in Parkinson disease

4. Three-dimensional reaching tasks: Effect of reaching height and width on upper limb kinematics and muscle activity

5. The Correlation Between Reach and Grasp Kinematic Measures and Clinical Measures of Manual Dexterity in Patients With Parkinson Disease

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