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Objective assessment of Parkinson’s Disease progression through feature analysis of the signal of an accelerometer monitoring daily patient’s life

  • Identification data

    Identifier:  TFG:5926
    Handle: https://hdl.handle.net/20.500.11797/TFG5926
    Authors:  Gonzalez Sanchez, Soraya
    Abstract:
    This project develops supervised learning algorithms capable of objectively classifying Parkinson's disease patients based on disease progression, using data from motion sensors placed on each patient. Additionally, it incorporates assessment data obtained during consultations with a neurologist. Objectively understanding the progression of Parkinson's disease is essential for improving the quality of life for patients and their families.

  • Others:

    Access rights: info:eu-repo/semantics/openAccess
    Education area(s): Enginyeria Biomèdica
    Department: Enginyeria Mecànica
    Entity: Universitat Rovira i Virgili (URV)
    Confidenciality: Si
    Subject: Parkinson, Malaltia de
    Project director: Valls, Aïda
    Work's public defense date: 2022-09-14
    Creation date in repository: 2023-05-04
    Academic year: 2021-2022
    Student: Gonzalez Sanchez, Soraya
    Work's codirector: Perez, Carlos

  • Keywords:

    Parkinson´s Disease
    supervised learning
    Biochemistry and biotechnology

  • Documents:

    Memòria

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