Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.
Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.
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通过 A 模式超声检查和卷积神经网络对经股截肢患者的步行模式进行分类。
DOI：
10.3390/s22239350
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发表时间：
2022-12-01
期刊：
影响因子：
--
通讯作者：
Lenzi T
中科院分区：
文献类型：
--
作者：
Murray R;Mendez J;Gabert L;Fey NP;Liu H;Lenzi T
许多人由于下肢截肢而与行动障碍作斗争。为了参与社会，他们需要能够在各种地形上行走，例如楼梯，坡道和平地。当前的下肢动力假肢需要不同的控制策略来改变截肢模式，并且使用来自假肢内的机械传感器的数据来确定用户处于哪种截肢模式。然而，区分安培模式可能具有挑战性。已经努力通过添加肌电图信息来提高分类准确度，但是这需要大量传感器，具有低信噪比，并且不能区分浅表和深层肌肉激活。另一种传感方式，A型超声，可以检测和区分浅表和深层肌肉的变化。在上肢姿势分类方面也取得了可喜的成果。尽管有这些优点，A型超声尚未用于下肢活动分类。在这里，我们表明，A型超声可以分类安培模式与可比的，并在某些情况下，上级精度机械感测。在这项研究中，7名经股截肢受试者在佩戴A型超声换能器、IMU传感器及其无源假体的情况下行走在安培环路上。该电路包括坐着，站立，平地行走，坡道上升，坡道下降，楼梯上升和楼梯下降，并训练时空卷积网络来连续分类这七种活动。离线连续分类与A型超声单独能够实现的准确性，相比，当单独使用运动学数据。结合运动学和超声产生的准确性。这表明，A型超声提供了额外的有用信息，用户的步态超出了机械传感器提供的信息，它可能能够提高american模式分类。通过将这些传感器结合到动力假肢中，用户可以享受他们的假肢的更高可靠性，以及在假肢模式之间更无缝的转换。
Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial–temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of , compared with , when using kinematic data alone. Combined kinematic and ultrasound produced accuracy. This suggests that A-mode ultrasound provides additional useful information about the user’s gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.
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影响因子：
11.8
作者：
Liu, Zuojun;Lin, Wei;Yang, Peng
通讯作者：
Yang, Peng
DOI：
10.1109/tbme.2012.2208641
发表时间：
2012-10
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Du L;Zhang F;Liu M;Huang H
通讯作者：
Huang H
影响因子：
2.5
作者：
Parker, P.;Englehart, K.;Hudgins, B.
通讯作者：
Hudgins, B.
DOI：
10.3390/s17092020
发表时间：
2017-09-04
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Liu M;Zhang F;Huang HH
通讯作者：
Huang HH
影响因子：
5.1
作者：
Cimolato, Andrea;Driessen, Josephus J. M.;Mattos, Leonardo S.;De Momi, Elena;Laffranchi, Matteo;De Michieli, Lorenzo
通讯作者：
De Michieli, Lorenzo