Computer Vision for Parkinson's Disease Evaluation: A Survey on Finger Tapping

Area of Science:

• Neurology
• Computer Science
• Biomedical Engineering

Background:

• Parkinson's disease (PD) is a progressive neurodegenerative disorder with increasing prevalence.
• Current PD diagnosis by neurologists is time-consuming and susceptible to human error due to delayed symptom appearance.
• Partial automation of PD assessment using computational methods, including finger tapping (FT) analysis, is being explored.

Purpose of the Study:

• To review recent advances in computer vision (CV) techniques applied to finger tapping (FT) for Parkinson's disease assessment.
• To provide insights into future research directions enabled by technological progress in this field.

Main Methods:

• Review of scientific literature on computer vision (CV) techniques.
• Analysis of studies utilizing finger tapping (FT) as a diagnostic tool for Parkinson's disease (PD).
• Exploration of artificial intelligence (AI) applications in PD assessment.

Main Results:

• Computer vision (CV) enables objective and potentially earlier assessment of finger tapping (FT) for Parkinson's disease (PD).
• AI and CV advancements offer new possibilities for developing automated PD diagnostic tools.
• The integration of CV in FT analysis shows promise for improving diagnostic accuracy and efficiency.

Conclusions:

• Advances in CV and AI are paving the way for more objective and efficient Parkinson's disease (PD) diagnosis.
• Future research should focus on further developing and validating CV-based FT assessment tools.
• Technological progress offers significant potential to support neurologists in the early detection of PD.