UTILIZING MULTIPLE MODALITIES FOR PARKINSON’S DETECTION

The research explores how machine learning methods can aid in the early identification of Parkinson's disease. It examines two distinct aspects: hand movements and vocal features. Unique datasets tracking the progressive changes in these symptoms over time are explored. Specialized techniques are employed to extract the most distinguishing hand motions and speech characteristics, which serve as potential biomarkers. In contrast to conventional approaches that depend exclusively on a single feature, this multi-modal approach combines both hand movement and voice biomarkers into a unified computational model. Overall, the research illustrates the promising potential of machine learning tools to enable earlier intervention for medical purposes, while emphasizing that the focus remains on aiding clinicians rather than replacing specialized assessments. The study does not aim at individual diagnosis but rather explores avenues for supporting healthcare professionals. Future research endeavors involve developing multi-modal models that encompass a wide range of aspects associated with this complex and variable condition.