Guo Jing-Yi, Zheng Yong-Ping, Xie Hong-Bo, Koo Terry K
New York Chiropractic College, New York, USA.
Prosthet Orthot Int. 2013 Feb;37(1):43-9. doi: 10.1177/0309364612446652. Epub 2012 Jun 8.
The inherent properties of surface electromyography limit its potential for multi-degrees of freedom control. Our previous studies demonstrated that wrist angle could be predicted by muscle thickness measured from B-mode ultrasound, and hence, it could be an alternative signal for prosthetic control. However, an ultrasound imaging machine is too bulky and expensive.
We aim to utilize a portable A-mode ultrasound system to examine the feasibility of using one-dimensional sonomyography (i.e. muscle thickness signals detected by A-mode ultrasound) to predict wrist angle with three different machine learning models - (1) support vector machine (SVM), (2) radial basis function artificial neural network (RBF ANN), and (3) back-propagation artificial neural network (BP ANN).
Feasibility study using nine healthy subjects.
Each subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute, respectively. Data obtained from 22.5 cycles/minute trials was used to train the models and the remaining trials were used for cross-validation. Prediction accuracy was quantified by relative root mean square error (RMSE) and correlation coefficients (CC).
Excellent prediction was noted using SVM (RMSE = 13%, CC = 0.975), which outperformed the other methods.
It appears that one-dimensional sonomyography could be an alternative signal for prosthetic control. Clinical relevance Surface electromyography has inherent limitations that prohibit its full functional use for prosthetic control. Research that explores alternative signals to improve prosthetic control (such as the one-dimensional sonomyography signals evaluated in this study) may revolutionize powered prosthesis design and ultimately benefit amputee patients.
表面肌电图的固有特性限制了其在多自由度控制方面的潜力。我们之前的研究表明，腕关节角度可以通过B型超声测量的肌肉厚度来预测，因此，它可能是用于假肢控制的替代信号。然而，超声成像设备体积太大且价格昂贵。
我们旨在利用便携式A型超声系统，研究使用一维超声成像法（即通过A型超声检测到的肌肉厚度信号），借助三种不同的机器学习模型——（1）支持向量机（SVM）、（2）径向基函数人工神经网络（RBF ANN）和（3）反向传播人工神经网络（BP ANN）来预测腕关节角度的可行性。
对9名健康受试者进行可行性研究。
每位受试者分别以每分钟15、22.5和30个周期的速度进行腕关节伸展运动。从每分钟22.5个周期的试验中获得的数据用于训练模型，其余试验用于交叉验证。预测准确性通过相对均方根误差（RMSE）和相关系数（CC）进行量化。
使用支持向量机获得了出色的预测结果（RMSE = 13%，CC = 0.975），其性能优于其他方法。
一维超声成像法似乎可以作为假肢控制的替代信号。临床意义 表面肌电图存在固有局限性，阻碍了其在假肢控制中的全面功能应用。探索替代信号以改善假肢控制的研究（如本研究中评估的一维超声成像信号）可能会彻底改变动力假肢的设计，并最终使截肢患者受益。
