  In recent years, sensors from smart consumer devices have shown great
diagnostic potential in movement disorders. In this context, data modalities
such as electronic questionnaires, hand movement and voice captures have
successfully captured biomarkers and allowed discrimination between Parkinson's
disease (PD) and healthy controls (HC) or differential diagnosis (DD). However,
to the best of our knowledge, a comprehensive evaluation of assessments with a
multi-modal smart device system has still been lacking. In a prospective study
exploring PD, we used smartwatches and smartphones to collect multi-modal data
from 504 participants, including PD patients, DD and HC. This study aims to
assess the effect of multi-modal vs. single-modal data on PD vs. HC and PD vs.
DD classification, as well as on PD group clustering for subgroup
identification. We were able to show that by combining various modalities,
classification accuracy improved and further PD clusters were discovered.
