A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control
A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control
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DOI：
10.1017/wtc.2022.18
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发表时间：
2022-09
影响因子：
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通讯作者：
Qiang Zhang;Natalie Fragnito;Xuefeng Bao;Nitin Sharma
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文献类型：
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作者：
Qiang Zhang;Natalie Fragnito;Xuefeng Bao;Nitin Sharma
摘要机器人辅助或康复设备是有前途的艾滋病患者的神经系统疾病，因为他们帮助恢复正常功能的上肢和下肢。然而，当使用这些机器人设备时，准确地估计人类意图或非侵入性的剩余努力仍然具有挑战性。在这篇文章中，我们提出了一种深度学习方法，该方法使用骨骼肌超声（US）成像的亮度模式（即B模式）来预测步行时踝关节的净跖屈力矩。定制深度卷积神经网络（CNN）的设计结构保证了深度学习方法的收敛性和鲁棒性。我们研究了US成像的感兴趣区域（ROI）对净跖屈力矩预测性能的影响。我们还比较了基于CNN的矩预测性能，利用B模式US和sEMG频谱成像具有相同的ROI大小。八名年轻参与者以多种速度在跑步机上行走的实验结果验证了通过使用所提出的US成像+深度学习方法进行净关节力矩预测来提高准确性。在相同的CNN结构下，与使用sEMG频谱成像的预测性能相比，US成像显著降低了归一化预测均方根误差37.55%（$ p $ < .001），并提高了预测决定系数20.13%（$ p $ < .001）。研究结果表明，美国成像+深度学习方法可以个性化评估人类关节自愿努力，可以与辅助或康复设备结合，以根据需要提供辅助控制策略来改善临床表现。
Abstract Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ($ p $ < .001) and increased the prediction coefficient of determination by 20.13% ($ p $ < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.