Accurately quantifying motor characteristics in Parkinson disease (PD) is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is a standard motor assessment. Clinicians visually evaluate a patient's tapping performance and assign an overall severity score based on tapping amplitude, speed, and irregularity. However, this subjective evaluation is prone to inter- and intra-rater variability, and does not offer insights into individual motor characteristics captured during this test. This paper introduces a granular computer vision-based method for quantifying PD motor characteristics from video recordings. Four sets of clinically relevant features are proposed to characterize hypokinesia, bradykinesia, sequence effect, and hesitation-halts. We evaluate our approach on video recordings and clinical evaluations of 74 PD patients from the Personalized Parkinson Project. Principal component analysis with varimax rotation shows that the video-based features corresponded to the four deficits. Additionally, video-based analysis has allowed us to identify further granular distinctions within sequence effect and hesitation-halts deficits. In the following, we have used these features to train machine learning classifiers to estimate the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS) finger-tapping score. Compared to state-of-the-art approaches, our method achieves a higher accuracy in MDS-UPDRS score prediction, while still providing an interpretable quantification of individual finger-tapping motor characteristics. In summary, the proposed framework provides a practical solution for the objective assessment of PD motor characteristics, that can potentially be applied in both clinical and remote settings. Future work is needed to assess its responsiveness to symptomatic treatment and disease progression.

翻译：准确量化帕金森病（PD）的运动特征对于监测疾病进展和优化治疗策略至关重要。手指敲击测试是一种标准的运动功能评估方法。临床医生通过视觉评估患者的敲击表现，并根据敲击幅度、速度和节律不规则性给出整体严重程度评分。然而，这种主观评估易受评估者间及评估者自身变异性的影响，且无法揭示测试过程中捕获的个体化运动特征。本文提出了一种基于计算机视觉的细粒度方法，用于从视频记录中量化PD运动特征。我们提出了四组临床相关特征，分别表征运动减少、运动迟缓、序列效应及犹豫-停顿现象。我们在个性化帕金森项目（Personalized Parkinson Project）的74名PD患者的视频记录和临床评估数据上验证了该方法。经方差最大旋转的主成分分析表明，基于视频的特征与上述四种功能障碍相对应。此外，视频分析使我们能够进一步区分序列效应和犹豫-停顿障碍中的细粒度差异。基于这些特征，我们训练了机器学习分类器来预测运动障碍学会统一帕金森病评定量表（MDS-UPDRS）的手指敲击评分。与现有先进方法相比，本方法在MDS-UPDRS评分预测中取得了更高的准确率，同时仍能提供可解释的个体化手指敲击运动特征量化结果。总之，该框架为PD运动特征的客观评估提供了一种实用解决方案，可潜在应用于临床和远程医疗场景。未来工作需要评估该方法对症状治疗及疾病进展的响应敏感性。