探讨计算机视觉检测脑卒中后补偿运动的可行性。

Hao-Ping Lin, Lina Zhao, Daniel Woolley, Xue Zhang, Hsiao-Ju Cheng, Weidi Liang, Christopher Kuah, Tegan Plunkett, Karen Chua, Lixin Zhang, Nicole Wenderoth
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引用次数: 0

摘要

代偿性运动通常在中风后观察到,并可能对长期运动恢复产生负面影响。在这种情况下,监测运动质量并提供反馈的系统将是有益的。在这项研究中,我们旨在使用传统的平板电脑摄像头和一种名为MediaPipe[1]的开源无标记身体姿势跟踪算法来检测坐着伸手过程中的补偿运动。根据偏瘫臂和非偏瘫臂之间的比较,我们对每帧中风患者的代偿性运动进行了注释。我们使用XGBoost算法训练了一个二元分类模型来检测代偿性运动,该模型在四名参与者的留一试验交叉验证中显示出0.92(SD 0.07)的平均准确度。尽管我们观察到了良好的模型性能,但在使用MediaPipe Pose时,我们也遇到了诸如地标缺失和错位等挑战。这项研究强调了在中风康复中使用简单摄像系统进行近实时补偿运动检测的可行性。需要做更多的工作来评估我们的方法在不同中风幸存者群体中的可推广性,并在移动设备上完全实现近乎实时的代偿性运动检测。
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Exploring the Feasibility of Computer Vision for Detecting Post-Stroke Compensatory Movements.

Compensatory movements are commonly observed post-stroke and can negatively affect long-term motor recovery. In this context, a system that monitors movement quality and provides feedback would be beneficial. In this study, we aimed to detect compensatory movements during seated reaching using a conventional tablet camera and an open-source markerless body pose tracking algorithm called MediaPipe [1]. We annotated compensatory movements of stroke patients per frame based on the comparison between the paretic and non-paretic arms. We trained a binary classification model using the XGBoost algorithm to detect compensatory movements, which showed an average accuracy of 0.92 (SD 0.07) in leave-one-trial-out cross-validation across four participants. Although we observed good model performance, we also encountered challenges such as missing landmarks and misalignment, when using MediaPipe Pose. This study highlights the feasibility of using near real-time compensatory movement detection with a simple camera system in stroke rehabilitation. More work is necessary to assess the generalizability of our approach across diverse groups of stroke survivors and fully implement near real-time compensatory movement detection on a mobile device.

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