Abstract
The heterogeneity of Parkinson’s disease (PD) generates significant challenges for accurate diagnosis, especially in early-stage disease, when symptoms may be very subtle. This study aimed to determine the accuracy of a convolutional neural network (CNN) technique based on a 6-min walk test (6MWT) using wearable sensors for distinguishing patients with early-stage PD (n = 78) from healthy controls (n = 50). Wearing six sensors, the participants performed the 6MWT, and the time-series data were converted into new images. The main results showed that the gyroscopic vertical component of the lumbar spine had the highest classification accuracy of 83.5%, followed by the thoracic spine (83.1%) and right thigh (79.5%) segment. These results suggest that the 6MWT and CNN models may pave the way for clinicians to diagnose and track PD symptoms earlier and thus provide timely treatment during the golden transition from geriatric to pathologic gait patterns.