Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

Journal: Journal of neurology
Published Date: Aug 14, 2024

Abstract

BACKGROUND: The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

Authors

  • Lazzaro di Biase
    Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy. l.dibiase@policlinicocampus.it.
  • Pasquale Maria Pecoraro
    Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.
  • Giovanni Pecoraro
    Telecommunications Engineer, Rome, Italy.
  • Syed Ahmar Shah
    Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
  • Vincenzo Di Lazzaro
    Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy.

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