A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
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DOI：
10.1109/tnsre.2021.3106900
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发表时间：
2021-01-01
影响因子：
4.9
通讯作者：
Sharma, Nitin
中科院分区：
文献类型：
--
作者：
Zhang, Qiang;Iyer, Ashwin;Sharma, Nitin
几十年来，表面肌电图（sEMG）一直是一种流行的非侵入性生物传感技术，用于预测人体关节运动。然而，串扰，相邻肌肉的干扰，以及无法测量深层肌肉限制了其预测关节运动的性能。最近，超声（US）成像已被提出作为一种替代的非侵入性技术，以预测关节运动，由于其高信噪比，直接可视化的目标组织，并能够进入深层肌肉。本文提出了一种双模式的方法，结合超声成像和表面肌电图预测意志动态踝关节背屈运动。三个功能集：1）具有四个sEMG特征的单模态集合，2）具有四个US成像特征的单模态集合，以及3）具有四个主要sEMG和US成像特征的双模态集合，以及测量的踝关节背屈角度，用于训练多个机器学习回归模型。实验结果表明，在不同的速度，从0.50米/秒至1.50米/秒，从坐姿和步行试验五个，双模态集显着降低了预测均方根误差（RMSE）。与单模态sEMG特征集相比，双模态集将坐姿的RMSE降低了47.84%，步行试验的RMSE降低了77.72%。同样，与US成像功能集相比，双模式集将坐姿的RMSE降低了53.95%，步行试验的RMSE降低了58.39%。研究结果表明，潜在的双模态传感方法可以被用作一个上级传感模式，以预测人类的意图的连续运动和实施的临床康复和辅助设备的意志控制。
For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.