Deep Learning for hand tracking in Parkinson's Disease video-based assessment: Current and future perspectives.

Journal: Artificial intelligence in medicine
Published Date: Jun 17, 2024

Abstract

BACKGROUND: Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment.

Authors

  • Gianluca Amprimo
    Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy; National Research Council - Institute of Electronics, Information Engineering and Telecommunications, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy. Electronic address: gianluca.amprimo@polito.it.
  • Giulia Masi
    Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. Electronic address: https://www.researchgate.net/profile/Giulia-Masi-2.
  • Gabriella Olmo
    Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy. Electronic address: https://www.sysbio.polito.it/analytics-technologies-health/.
  • Claudia Ferraris
    National Research Council - Institute of Electronics, Information Engineering and Telecommunications, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy. Electronic address: https://www.ieiit.cnr.it/people/Ferraris-Claudia.

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