Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is, in most cases, an early stage of Parkinson’s disease or related disorders. Diagnosis requires an overnight video-polysomnogram (vPSG), however, even for sleep experts, interpreting vPSG data is challenging. Using a 3D camera, automated analysis of movements has yielded high accuracy. We aimed to replicate and extend prior work using a conventional 2D camera. The dataset included 172 vPSG recordings from a clinical sleep center, 81 patients with iRBD and 91 non-RBD healthy controls. An optical flow computer vision algorithm automatically detected movements during REM sleep, from which features of rate, ratio, magnitude and velocity of movements, and ratio of immobility were extracted. Patients with iRBD exhibited an increased number of shorter movements and immobility periods. Accuracies for detecting iRBD ranged from 84.9% (with 2 features) to 87.2% (with 5 features). Combining all 5 features but only analyzing short (0.1-2 second duration) movements achieved the highest accuracy at 91.9%. This approach could be implemented in clinical sleep laboratories to facilitate and improve the diagnosis of iRBD. Read more