Use of Sonomyographic Sensing to Estimate Knee Angular Velocity During Varying Modes of Ambulation*
Use of Sonomyographic Sensing to Estimate Knee Angular Velocity During Varying Modes of Ambulation*
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DOI：
10.1109/embc44109.2020.9176674
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Kaitlin G. Rabe;M. H. Jahanandish;K. Hoyt;Nicholas P. Fey
中科院分区：
文献类型：
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作者：
Kaitlin G. Rabe;M. H. Jahanandish;K. Hoyt;Nicholas P. Fey
肌肉的超声（US）成像已经被引入作为用于辅助设备控制的有前途的感测模态。十名身体健全的受试者在运动捕捉实验室的跑步机上完成水平，倾斜和倾斜行走，同时在上半身和下半身佩戴反光标记。一个可穿戴的US传感器被固定到受试者的大腿前部，从膝伸肌的横向US图像中提取时间-强度特征。这些特征用于训练和测试高斯过程回归模型，以连续估计膝关节屈曲/伸展角速度。评估了四个回归模型：（1）受试者依赖性/任务特异性，（2）受试者依赖性/合并任务，（3）受试者独立性/任务特异性，和（4）受试者独立性/合并任务。独立于受试者的模型被“调整”了多达六个测试对象的数据，以提高性能。采用双因素方差分析检验评估每种方法对膝关节角速度估计值（α=0.05）的均方根误差（RMSE）的影响。完成了统计参数标测（SPM），以比较作为步态周期函数的实际与估计膝关节角速度（α=0.05）。对于倾斜和水平行走，受试者依赖/池任务模型导致最低的错误，而受试者依赖/特定任务模型导致最低的错误下降行走。令人印象深刻的是，两个因素的测试没有发现特定任务和池任务模型之间的差异。此外，尽管捕获了许多重要的特征，膝关节速度在个人之间，有，正如预期的那样，受试者依赖和受试者独立的模型之间的显着差异。总的来说，这些结果是有前途的潜在辅助设备控制与误差率<10%的所有回归models.Clinical Relevance-这项工作是第一个研究，证明使用基于超声的传感估计膝关节角速度在多种模式的american的可行性。
Ultrasound (US) imaging of muscle has been introduced as a promising sensing modality for assistive device control. Ten able-bodied subjects completed level, incline and decline walking on a treadmill in a motion capture laboratory while wearing reflective markers on upper- and lower-body. A wearable US transducer was affixed to subjects’ anterior thigh, and time-intensity features were extracted from transverse US images of the knee extensor muscles. These features were used to train and test Gaussian process regression models for continuous estimation of knee flexion/extension angular velocity. Four regression models were evaluated: (1) subject-dependent/task-specific, (2) subject-dependent/pooled-tasks, (3) subject-independent/task-specific, and (4) subject-independent/pooled-tasks. Subject-independent models were "tuned" with up to six strides of the test subject’s data to boost performance. A two-factor analysis of variance test was used to assess the effect of each approach on root mean square error (RMSE) of estimated knee angular velocity (α=0.05). Statistical parametric mapping (SPM) was completed to compare actual vs. estimated knee angular velocity as a function of the gait cycle (α=0.05). For incline and level walking, the subject-dependent/pooled-tasks model resulted in the lowest error while the subject-dependent/task-specific model resulted in the lowest error for decline walk. Impressively, the two-factor test revealed no difference between task-specific and pooled-task models. Furthermore, despite capturing many important features of knee velocity across individuals there were, as expected, significant differences between subject-dependent and subject-independent models. Collectively, these results are promising for potential assistive device control with error rates <10% for all regression models that were tested.Clinical Relevance—This work is the first study to demonstrate the feasibility of using ultrasound-based sensing for estimation of knee angular velocity during multiple modes of ambulation.