Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model
Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model
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DOI：
10.1109/embc46164.2021.9630046
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Qiang Zhang;Natalie Fragnito;Alison Myers;Nitin Sharma
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作者：
Qiang Zhang;Natalie Fragnito;Alison Myers;Nitin Sharma
许多康复外骨骼使用非侵入性表面肌电图（sEMG）来测量人类意志意图。然而，来自相邻肌肉群的信号干扰sEMG测量。此外，无法测量来自深层肌肉的sEMG信号可能无法准确地测量意志意图。在这项工作中，我们结合表面肌电信号和超声（US）成像衍生的信号，以提高自愿踝关节努力的预测准确性。我们使用了一个多变量线性模型（MLM），结合表面肌电信号和超声信号的踝关节净跖屈（PF）时刻预测在步行站姿阶段。我们假设，所提出的sEMG-US成像驱动的MLM将导致比sEMG驱动和US成像驱动的MLM更准确的净PF矩预测。同步测量，包括反射标记坐标，地面反作用力，表面肌电信号的外侧/内侧腓肠肌（LGS/MGS），和比目鱼肌（SOL）的肌肉，和美国成像的LGS和SOL肌肉从五个健全的参与者在跑步机上以多种速度行走。踝关节净PF矩基准基于逆动力学计算，而净PF矩预测由sEMG-US成像驱动、sEMG驱动和US成像驱动的MLMs确定。研究结果表明，sEMG-US成像驱动的MLM可以显着提高在多个速度的步行站姿阶段的净PF时刻的预测。潜在地，所提出的sEMG-US成像驱动的MLM可以在用于康复外骨骼的高级和智能控制策略中用作上级关节运动意图模型。
Many rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.