Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control
Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control
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DOI：
10.1109/jbhi.2019.2891997
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发表时间：
2019-11-01
影响因子：
7.7
通讯作者：
Hoyt, Kenneth
中科院分区：
文献类型：
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作者：
Jahanandish, Mohammad Hassan;Fey, Nicholas P.;Hoyt, Kenneth
目的：提高电动辅助器具的操作直观性，以提高其临床应用。因此，应识别个体的意图，并且设备运动应遵循它。骨骼肌协同收缩以产生定义的下肢运动，因此下肢肌肉组织中的独特收缩模式可以提供设备关节控制的手段。超声（US）成像能够直接测量肌肉节段的局部变形。因此，本研究的目的是评估使用US估计人类下肢运动的可行性。研究方法：一种新的算法被开发来计算股直肌在非负重膝关节屈伸实验中的US特征。研究了骨骼肌组织的五个超声特征，即厚度、腱膜间角、羽状角、肌束长度和回声。利用多尺度脊滤波器提取图像中的结构，并使用随机抽样一致性（RANSAC）模型分割肌肉腱膜和肌束。定位方案进一步引导RANSAC实现在US图像序列中的跟踪。高斯过程回归模型使用分段特征来训练以估计膝关节角度和角速度。结果如下：所提出的分割估计方法可以估计膝关节角度和角速度的平均均方根误差值为7.45和0.262拉德，分别。平均处理速率为36帧，这是有前途的实时实现。结论：实验结果证明了利用超声估计人体下肢运动的可行性。该算法在真实的时间中工作的能力可以使得能够使用US作为用于下肢应用的神经接口。重要性：使用可穿戴US成像的人类下肢运动的直观意图识别可以使意志辅助设备控制成为可能，并增强那些有移动障碍的人的运动结果。
Objective: Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements. Methods: A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexionextension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity. Results: The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45 and 0.262 rads, respectively. The average processing rate was 36 framess that is promising toward real-time implementation. Conclusion: Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications. Significance: Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.