Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 1840 Entrepreneur Dr., 27695, Raleigh, NC, USA.
Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 333 S Columbia St., 27514, Chapel Hill, NC, USA.
J Neuroeng Rehabil. 2022 Aug 9;19(1):86. doi: 10.1186/s12984-022-01061-z.
Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy.
The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.
Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.
On average, the normalized moment prediction root mean square error was reduced by 14.58 % ([Formula: see text]) and 36.79 % ([Formula: see text]) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.
The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
提高人机界面（HMI）的预测能力对于实现基于生物启发或基于模型的康复干预控制策略至关重要，这些策略对于辅助神经损伤后的肢体功能越来越感兴趣。HMI 的一个基本作用是通过将来自机械传感器或表面肌电图（sEMG）传感器的信号进行映射，准确预测人类意图。这些传感器仅限于测量产生的肢体力或运动或引起力的神经信号。由于 HMI 中的中间映射还取决于肌肉收缩性，因此存在将肌肉的结构特征作为动态肌肉运动的替代物包含在内的动机，从而进一步提高 HMI 的预测准确性。
本研究旨在研究一种非侵入性的 sEMG 和超声（US）成像驱动的 Hill 型神经肌肉模型（HNM），用于预测踝关节跖屈净力矩。我们假设 sEMG 和 US 成像信号的融合比单独的 sEMG 或 US 成像产生更准确的净跖屈力矩预测。
10 名年轻非残疾参与者以 0.50、0.75、1.00、1.25 和 1.50 m/s 的速度在跑步机上行走。所提出的 HNM 由两个肌肉肌腱单元组成。每个单元的肌肉激活被计算为归一化 sEMG 信号和 US 成像归一化肌肉厚度信号的加权和。HNM 校准在单速模式和变速模式下进行，然后在所有行走速度下验证校准的 HNM。
平均而言，与 sEMG 驱动和 US 成像驱动的 HNM 相比，所提出的 HNM 将归一化力矩预测均方根误差分别降低了 14.58%([Formula: see text])和 36.79%([Formula: see text])。此外，与单速模式相比，变速模式下的数据校准模型对于力矩预测更为稳健。
所提出的 sEMG-US 成像驱动的 HNM 可以显著提高多个行走速度下的净跖屈力矩预测精度。研究结果表明，由于其优越的预测能力，所提出的 HNM 可潜在用于康复设备的基于生物启发的控制策略。
