This study investigates the application of general human motion encoders trained on large-scale human motion datasets for analyzing gait patterns in PD patients. Although these models have learned a wealth of human biomechanical knowledge, their effectiveness in analyzing pathological movements, such as parkinsonian gait, has yet to be fully validated. We propose a comparative framework and evaluate six pre-trained state-of-the-art human motion encoder models on their ability to predict the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) gait scores from motion capture data. We compare these against a traditional gait feature-based predictive model in a recently released large public PD dataset, including PD patients on and off medication. The feature-based model currently shows higher weighted average accuracy, precision, recall, and F1-score. Motion encoder models with closely comparable results demonstrate promise for scalability and efficiency in clinical settings. This potential is underscored by the enhanced performance of the encoder model upon fine-tuning on PD training set. Four of the six human motion models examined provided prediction scores that were significantly different between on- and off-medication states. This finding reveals the sensitivity of motion encoder models to nuanced clinical changes. It also underscores the necessity for continued customization of these models to better capture disease-specific features, thereby reducing the reliance on labor-intensive feature engineering. Lastly, we establish a benchmark for the analysis of skeleton-based motion encoder models in clinical settings. To the best of our knowledge, this is the first study to provide a benchmark that enables state-of-the-art models to be tested and compete in a clinical context. Codes and benchmark leaderboard are available at code.

翻译：本研究探讨了利用在大规模人体运动数据集上训练的通用人体运动编码器来分析帕金森病患者步态模式的应用。尽管这些模型已学习到丰富的人体生物力学知识，但其在分析病理性运动（如帕金森步态）方面的有效性尚未得到充分验证。我们提出了一个比较框架，并评估了六种预训练的最先进人体运动编码器模型根据运动捕捉数据预测运动障碍学会-统一帕金森病评定量表（MDS-UPDRS-III）步态评分的能力。我们在一个近期发布的大型公共帕金森病数据集上，将这些模型与一个基于传统步态特征的预测模型进行了比较，该数据集包括用药和未用药的帕金森病患者。目前，基于特征的模型显示出更高的加权平均准确率、精确率、召回率和F1分数。结果具有相近可比性的运动编码器模型展现了在临床环境中可扩展性和效率方面的潜力。该潜力通过编码器模型在帕金森病训练集上微调后性能得到提升而凸显。所考察的六个人体运动模型中有四个提供的预测评分在用药和未用药状态之间存在显著差异。这一发现揭示了运动编码器模型对细微临床变化的敏感性。它也强调了持续定制这些模型的必要性，以更好地捕捉疾病特异性特征，从而减少对劳动密集型特征工程的依赖。最后，我们为临床环境中基于骨架的运动编码器模型的分析建立了一个基准。据我们所知，这是首个提供基准测试的研究，使得最先进的模型能够在临床背景下进行测试和竞争。代码和基准排行榜可在代码链接处获取。