Abstract：
BACKGROUND: Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment.
OBJECTIVE: To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences.
METHODS: Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined.
RESULTS: Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude.
CONCLUSIONS: This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.
OBJECTIVE: To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences.
METHODS: Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined.
RESULTS: Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude.
CONCLUSIONS: This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.
摘要：
背景：通常从录像中评估患有运动障碍脑瘫（CP）的儿童和青少年的运动障碍，然而，评分是耗时的，需要专业知识才能进行适当的评估。
目的：探索一种机器学习方法，用于在短视频序列中自动分类远端腿部肌张力障碍和舞蹈性病变的幅度和持续时间。
方法：使用无标记运动分析对可用的足趾轻敲任务视频进行了预处理，以优化关键点提取。将后处理的关键点数据传递给时间序列分类集成算法，以对肌张力障碍和舞蹈性关节炎的持续时间和幅度类别（得分0、1、2、3和4）进行分类。分别。作为基础事实，使用运动障碍障碍量表对肌张力障碍和舞蹈性关节炎进行临床评分。确定了多类别绩效指标以及汇总得分的指标：缺席（得分0）和存在（得分1-4）。
结果：包括33名参与者：29名患有运动障碍CP，4名通常正在发展，年龄14岁：6个月±5岁：15个月。肌张力障碍的多类准确性结果持续时间为77％，幅度为68％；对于舞蹈性关节炎，持续时间为30％，幅度为38％。得分0与得分1至4的指标显示，肌张力障碍持续时间的准确率为81%，77％的肌张力障碍振幅，53％为舞蹈症的持续时间和幅度。
结论：这项方法学研究在区分肌张力障碍的存在和不存在方面取得了令人鼓舞的结果。但不是因为卵巢狭窄症.模型需要更大的数据集才能准确表示不同的类别/分数。这项研究提出了一种仅从视频数据自动评估运动障碍的新颖方法。
目的：探索一种机器学习方法，用于在短视频序列中自动分类远端腿部肌张力障碍和舞蹈性病变的幅度和持续时间。
方法：使用无标记运动分析对可用的足趾轻敲任务视频进行了预处理，以优化关键点提取。将后处理的关键点数据传递给时间序列分类集成算法，以对肌张力障碍和舞蹈性关节炎的持续时间和幅度类别（得分0、1、2、3和4）进行分类。分别。作为基础事实，使用运动障碍障碍量表对肌张力障碍和舞蹈性关节炎进行临床评分。确定了多类别绩效指标以及汇总得分的指标：缺席（得分0）和存在（得分1-4）。
结果：包括33名参与者：29名患有运动障碍CP，4名通常正在发展，年龄14岁：6个月±5岁：15个月。肌张力障碍的多类准确性结果持续时间为77％，幅度为68％；对于舞蹈性关节炎，持续时间为30％，幅度为38％。得分0与得分1至4的指标显示，肌张力障碍持续时间的准确率为81%，77％的肌张力障碍振幅，53％为舞蹈症的持续时间和幅度。
结论：这项方法学研究在区分肌张力障碍的存在和不存在方面取得了令人鼓舞的结果。但不是因为卵巢狭窄症.模型需要更大的数据集才能准确表示不同的类别/分数。这项研究提出了一种仅从视频数据自动评估运动障碍的新颖方法。
