In Vitro Diagnosis of Parkinson's Disease Based on Facial Expression and Behavioral Gait Data

Parkinson's disease (PD) is characterized by incurable, rapid progression, and severe disability, severely impacting the lives of patients and their families. With an aging population, the need for early detection of PD is increasing. In vitro diagnosis has attracted attention because of its non-invasiveness and low cost, but there are some problems with the existing methods: 1) facial expression diagnosis has little training data; 2) gait diagnosis requires specialized equipment and acquisition environment, which is poorly generalizable; 3) a single modality is easy to miss the diagnosis; and 4) multimodal diagnostic methods are not universally applicable. To address the above issues, we propose a novel multimodal in vitro diagnostic method for PD based on facial expression and behavioral gait. The method uses a lightweight deep learning model for feature extraction and feature fusion to improve diagnostic accuracy and ease of use. Meanwhile, we have established the largest multimodal PD data set in collaboration with hospitals and conducted a large number of experiments to verify the effectiveness of the method.