A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network
A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network
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DOI：
10.1109/tnsre.2023.3248647
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发表时间：
2023-02
影响因子：
4.9
通讯作者：
Joel Mendez;Rosemarie Murray;Lukas Gabert;Nicholas P. Fey;Honghai Liu;T. Lenzi
中科院分区：
文献类型：
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作者：
Joel Mendez;Rosemarie Murray;Lukas Gabert;Nicholas P. Fey;Honghai Liu;T. Lenzi
下肢动力假肢可以为使用者提供对假肢的意志控制。为了实现这一目标，他们需要一个传感模态，可靠地解释用户的意图移动。表面肌电图（EMG）先前已被提出来测量肌肉兴奋，并提供意志控制上肢和下肢动力假肢用户。不幸的是，EMG遭受低信噪比和相邻肌肉之间的串扰，通常限制了基于EMG的控制器的性能。超声已被证明具有比表面EMG更好的分辨率和特异性。然而，这项技术尚未被整合到下肢假肢中。在这里，我们表明，A型超声传感可以可靠地预测假体行走运动学的个人与经股截肢。本文应用A型超声对9例经股动脉截肢者的残肢进行了带被动假肢行走时的超声检查。通过回归神经网络将超声特征映射到关节运动学。对训练模型与未训练运动学的测试显示了对膝关节位置、膝关节速度、踝关节位置和踝关节速度的准确预测，归一化RMSE分别为9.0 ± 3.1%、7.3 ± 1.6%、8.3 ± 2.3%和10.0 ± 2.5%。这种基于超声的预测表明，A模式超声是一种用于识别用户意图的可行感测技术。这项研究是第一个必要的一步，实现自主假体控制器的基础上，A型超声个人经股截肢。
Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 ± 3.1%, 7.3 ± 1.6%, 8.3 ± 2.3%, and 10.0 ± 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation.