在水平,倾斜和下降行走任务中使用便携式声速传感的步态相位识别

M. H. Jahanandish, Kaitlin G. Rabe, Nicholas P. Fey, K. Hoyt
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引用次数: 9

摘要

动力下肢辅助装置的临床可行性需要可靠和直观的控制策略。站立和摆动是不同运动任务中步态周期的主要阶段。因此,一种可靠的方法来准确地识别这些相位可以降低传感复杂性,并有助于实现辅助设备的高级控制。超声(US)成像最近被引入作为一种新的传感方式,可以为直观的设备控制提供解决方案。在水平、倾斜和下降行走任务中收集人类股直肌和股中间肌的超声图像。相对于参考图像,测量US图像的5个低水平静态(即时间无关)特征,包括相关系数、绝对差和、结构相似指数、差平方和和图像回声度。静态特征的时间导数也作为时间特征计算。使用这些静态特征训练支持向量机分类器来识别依赖和独立于行走任务的步态阶段。结果表明,当仅使用静态特征进行训练时,任务独立分类器识别步态阶段的准确率为88.3%。使用时间特征(p $\lt0.01)$后,分类器的性能显著提高到92.8%。算法效率高,平均处理速度大于100 Hz。这项研究首次展示了使用US成像技术对多个表面的移动阶段进行连续估计。这些发现表明,任务独立的方法可以可靠地识别步态周期的主要阶段。这一研究领域的进展可能为高级辅助装置控制提供更简单的直观策略,并增加其临床相关性。
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Gait Phase Identification During Level, Incline and Decline Ambulation Tasks Using Portable Sonomyographic Sensing
Clinical viability of powered lower-limb assistive devices requires reliable and intuitive control strategies. Stance and swing are the main phases of the gait cycle across different locomotion tasks. Hence, a reliable method to accurately identify these phases can decrease sensing complexity and assist in enabling high-level control of assistive devices. Ultrasound (US) imaging has recently been introduced as a new sensing modality that may provide a solution for intuitive device control. US images of the rectus femoris and vastus intermedius muscles were collected in humans during level, incline, and decline ambulation tasks. Five low-level static (i.e. time-independent) features of US images were measured with respect to a reference image, including correlation coefficient, sum of absolute differences, structural similarity index, sum of squared differences, and image echogenicity. Time-derivatives of the static features were also calculated as temporal features. Support vector machine classifiers were trained using these static features to identify the gait phase both dependent and independent of the ambulation tasks. The results indicate an accuracy of 88.3% in identifying the gait phases for task-independent classifiers when trained using only the static features. Performance of the classifiers improved significantly to 92.8% after using the temporal features (p $\lt0.01)$. The algorithm was efficient and the average processing speed was faster than 100 Hz. This study is the first demonstration on use of US imaging to provide continuous estimates of ambulation phase, and on multiple surfaces. These findings suggest task-independent approaches may reliably identify the main phases of the gait cycle. Advancements in this area of study may provide simpler intuitive strategies for high-level assistive device control and increase their clinical relevance.
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