Early Detection of Parkinson’s Disease Using a Single-Arm Wearable Sensor and Convolutional Neural Networks

Abstract Background Early-stage Parkinson’s disease (PD) is characterized by subtle motor symptoms that complicate diagnosis and often delay intervention. Timely and accurate identification is critical for effective management, emphasizing the need for objective, non-invasive diagnostic methods. Methods This study developed a non-invasive approach for early PD detection using wearable sensors and a convolutional neural network (CNN) during a 6-min walk test. The test was segmented into 1-min intervals, extracting three straight-walking and three turning gait phases per minute. Time-series data were collected from 78 patients with early-stage PD and 50 healthy controls across six body locations. Results The CNN achieved 95.6% accuracy when classifying PD status using gyroscope data from the left arm during the first-minute straight-walking phase. Furthermore, repeated-measures analysis of variance and post hoc tests indicated that a 1- to 2-min measurement window was sufficient for reliable detection, supporting the feasibility of time-efficient clinical screening. Conclusions These findings suggest that a wearable sensor, placed on a single arm and used to capture first-minute straight gait data, can provide highly accurate and non-invasive early PD detection. Future research should evaluate medication effects, extend validation to broader disease stages, and explore unsupervised learning approaches to identify latent motor phenotypes and enable personalized monitoring.