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Parkinson's disease (PD) is a neurological disorder that has a variety ofobservable motor-related symptoms such as slow movement, tremor, muscularrigidity, and impaired posture. PD is typically diagnosed by evaluating theseverity of motor impairments according to scoring systems such as the MovementDisorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).Automated severity prediction using video recordings of individuals provides apromising route for non-intrusive monitoring of motor impairments. However, thelimited size of PD gait data hinders model ability and clinical potential.Because of this clinical data scarcity and inspired by the recent advances inself-supervised large-scale language models like GPT-3, we use human motionforecasting as an effective self-supervised pre-training task for theestimation of motor impairment severity. We introduce GaitForeMer, GaitForecasting and impairment estimation transforMer, which is first pre-trainedon public datasets to forecast gait movements and then applied to clinical datato predict MDS-UPDRS gait impairment severity. Our method outperforms previousapproaches that rely solely on clinical data by a large margin, achieving an F1score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, weshow how public human movement data repositories can assist clinical use casesthrough learning universal motion representations. The code is available athttps://github.com/markendo/GaitForeMer .

GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation