Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning

The quantitative assessment of Parkinson's disease (PD) is critical for guiding diagnosis, treatment, and rehabilitation. Conventional clinical evaluations-heavily dependent on manual rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS)-are time-consuming and prone to inter-rater variability. In this study, we propose a fully automated UPDRS gait-scoring framework. Our method combines (a) surface electromyography (EMG) signals and (b) inertial measurement unit (IMU) data into a single deep learning model. Our end-to-end network comprises three specialized branches-a diagnosis head, an evaluation head, and a balance head-whose outputs are integrated via a customized fusion-detection module to emulate the multidimensional assessments performed by clinicians. We validated our system on 21 PD patients and healthy controls performing a simple walking task while wearing a four-channel EMG array on the lower limbs and 2 shank-mounted IMUs. It achieved a mean classification accuracy of 92.8% across UPDRS levels 0-2. This approach requires minimal subject effort and sensor setup, significantly cutting clinician workload associated with traditional UPDRS evaluations while improving objectivity. The results demonstrate the potential of wearable sensor-driven deep learning methods to deliver rapid, reliable PD gait assessment in both clinical and home settings.