Early-stage Parkinson's disease detection based on optical flow and video vision transformes
Résumé
Hypomimia, a symptom of Parkinson’s disease (PD), s marked by reduced facial movements and loss of face emotional expressions. This study focuses on identifying hypomimia in individuals with early-stage PD using optical-flow-based video vision transformer. Our study included video recordings from 109 PD and 45 healthy control (HC) subjects with an average of two videos per person (294 videos in total). The participants asked to speak freely while being recorded. To extract typical facial muscle movements from subjects, we computed the optical flow (OF) from the videos. Video vision transformer is then used to infer feature representations from OF and RGB modalities, input to a Random Forest (RF) classifier to classify PD vs. HC. We obtained classification scores up to 83% in terms of balanced accuracy (BA) and an area under the curve (AUC) of 84% at subject level. The results are promising for identifying hypomimia in the early stages of PD, and this research could lead to the possibility of continuous monitoring of hypomimia outside of hospital settings via telemedicine.
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