Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5700-5703. doi: 10.1109/EMBC46164.2021.9630563.
Older adults with dementia have a high risk of developing drug-induced parkinsonism; however, formal clinical gait assessments are too infrequent to capture fluctuations in their gait. Camera-based human pose estimation and tracking provides a means to frequently monitor gait in nonclinical settings. In this study, 2160 walking bouts from 49 participants were recorded using a ceiling-mounted camera. Recorded color videos were processed using AlphaPose to obtain 2D joint trajectories of the participant as they were walking down a hallway of the unit. A subset of 324 walking bouts from 14 participants were annotated with clinical scores of parkinsonism on the Unified Parkinson's Disease Rating Scale (UPDRS)-gait scale. Linear, random forest, and ordinal logistic regression models were evaluated for regression to UPDRS-gait scores using engineered 2D gait features calculated from the AlphaPose joint trajectories. Additionally, spatial temporal graph convolutional networks (ST-GCNs) were trained to predict UPDRS-gait scores from joint trajectories and gait features using a two-stage training scheme (self-supervised pretraining stage on all walks followed by a finetuning stage on labelled walks). All models were trained using leave-one-subject-out cross-validation to simulate testing on previously unseen participants. The macro-averaged F1-score was 0.333 for the best model operating on only gait features and 0.372 for the top ST-GCN model that used both joint trajectories and gait features as input. When accepting predicted scores that were only off by at most 1 point on the UPDRS-gait scale, the accuracy of the model that only used gait features was 82.8%, while the model that also used joint trajectories had an accuracy of 94.2%.Clinical Relevance- The combination of gait features and joint trajectories capture parkinsonian qualities in gait better than either group of data individually.
患有痴呆症的老年人发生药物诱导性帕金森病的风险较高；然而，正式的临床步态评估过于频繁，无法捕捉其步态的波动。基于摄像头的人体姿势估计和跟踪为在非临床环境中频繁监测步态提供了一种手段。在这项研究中，使用天花板安装的摄像头记录了 49 名参与者的 2160 次行走。记录的彩色视频通过 AlphaPose 进行处理，以获取参与者在单位走廊上行走时的 2D 关节轨迹。从 14 名参与者中选择了 324 次行走，并用帕金森病统一评定量表（UPDRS）-步态量表对其进行了帕金森病的临床评分标注。使用从 AlphaPose 关节轨迹计算的工程化 2D 步态特征，评估了线性、随机森林和有序逻辑回归模型对 UPDRS-步态评分的回归。此外，使用两阶段训练方案（所有行走的自我监督预训练阶段，然后是带标注行走的微调阶段），使用时空图卷积网络（ST-GCN）从关节轨迹和步态特征预测 UPDRS-步态评分。所有模型都使用留一受试者交叉验证进行训练，以模拟对以前未见的受试者的测试。仅使用步态特征的最佳模型的宏平均 F1 得分为 0.333，而同时使用关节轨迹和步态特征作为输入的最佳 ST-GCN 模型的得分为 0.372。当接受 UPDRS-步态量表上仅相差 1 分的预测评分时，仅使用步态特征的模型的准确率为 82.8%，而同时使用关节轨迹的模型的准确率为 94.2%。临床意义-步态特征和关节轨迹的组合比任何一组数据单独更能捕捉到帕金森步态的特征。
