Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains
Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains
复制标题
用于在多地形上行走期间连续估计关节扭矩的声肌图和肌电图传感性能
DOI：
10.1109/tnsre.2021.3134189
复制
发表时间：
2021
影响因子：
4.9
通讯作者：
Fey, Nicholas P.
中科院分区：
文献类型：
--
作者：
Rabe, Kaitlin G.;Lenzi, Tommaso;Fey, Nicholas P.
电动辅助设备技术的进步，包括向单个设备内的多个关节提供净机械动力的能力，有可能显著提高移动性并恢复用户的独立性。然而，这些设备依赖于用户同时连续控制多个动力腿部关节的能力。此类方法的成功依赖于对用户意图的可靠感知和对设备控制参数的准确映射。在这里，我们比较了两种非侵入性传感模式：表面肌电图和声学图(即骨骼肌的超声成像)，作为高斯过程回归模型的输入，这些模型被训练来估计在不同形式的行走中的髋关节、膝关节和踝关节的时刻。在完成水平、倾斜(10°)和下降(10°)步行试验的同时，对10名非残疾个体进行了表面肌电信号和声学传感器的测试。结果提示，大腿前后部肌肉声学图可以比表面肌电图法更准确地估计髋关节、膝关节和踝关节的力矩。此外，这些结果可以通过以与任务无关的方式训练高斯过程回归模型来实现；即，在同一预测框架内结合水平行走和坡道行走的特征。这些发现支持在电动辅助设备中集成声学和肌电传感，以持续控制关节扭矩。
Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.