Rabe Kaitlin G, Hassan Jahanandish Mohammad, Hoyt Kenneth, Fey Nicholas P
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3799-3802. doi: 10.1109/EMBC44109.2020.9176674.
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.
肌肉的超声（US）成像已被引入，作为一种用于辅助设备控制的有前景的传感方式。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了水平、上坡和下坡行走，同时在上半身和下半身佩戴反光标记。一个可穿戴式超声换能器固定在受试者的大腿前部，并从膝伸肌的横向超声图像中提取时间强度特征。这些特征被用于训练和测试高斯过程回归模型，以连续估计膝关节屈伸角速度。评估了四个回归模型：（1）受试者依赖/特定任务，（2）受试者依赖/合并任务，（3）受试者独立/特定任务，以及（4）受试者独立/合并任务。受试者独立模型用测试受试者的数据最多六个步幅进行“调整”以提高性能。使用双因素方差分析测试来评估每种方法对估计膝关节角速度的均方根误差（RMSE）的影响（α = 0.05）。完成统计参数映射（SPM）以比较实际与估计的膝关节角速度作为步态周期的函数（α = 0.05）。对于上坡和水平行走，受试者依赖/合并任务模型的误差最低，而对于下坡行走，受试者依赖/特定任务模型的误差最低。令人印象深刻的是，双因素测试显示特定任务模型和合并任务模型之间没有差异。此外，尽管捕捉了个体间膝关节速度的许多重要特征，但正如预期的那样，受试者依赖模型和受试者独立模型之间存在显著差异。总体而言，这些结果对于所有测试的回归模型以低于10%的错误率进行潜在辅助设备控制很有前景。临床相关性——这项工作是第一项证明在多种步行模式下使用基于超声的传感来估计膝关节角速度的可行性的研究。
