Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical potential. Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity. We introduce GaitForeMer, Gait Forecasting and impairment estimation transforMer, which is first pre-trained on public datasets to forecast gait movements and then applied to clinical data to predict MDS-UPDRS gait impairment severity. Our method outperforms previous approaches that rely solely on clinical data by a large margin, achieving an F1 score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we show how public human movement data repositories can assist clinical use cases through learning universal motion representations. The code is available at https://github.com/markendo/GaitForeMer .

翻译：Parkinson病(PD)是一种神经病,具有各种可观察到的与运动有关的运动症状,如运动缓慢、颤抖、肌肉僵硬和姿势受损等。PD通常通过根据运动失常学会Parkinson疾病评分系统(MDS-UPDRS)等评分系统评估马达损伤的严重程度来诊断。使用个人视频记录进行自动严重程度预测,为非侵入性监测马达损伤提供了一条很有希望的途径。然而,PD 步数数据规模有限,阻碍了模型能力和临床潜力。由于这种临床数据稀缺,并受到诸如GPT-3等自上一级大型语言模型的最新进展的启发,我们使用人类运动预测作为评估马达损伤严重程度的有效自我超前训练任务。我们介绍GaitForeMer, Gait Survey Surveyingingingings and descriction Translations,我们使用SlationFlationFlationFloralde,我们使用以前的精确度数据,我们使用以前的数据比GlationMFlationFlation1,我们使用以前的数据显示了以前的数据。