Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics
Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics
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DOI：
10.1109/access.2023.3246950
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
R. Roy;Derek G. Kamper;Xiaogang Hu
中科院分区：
文献类型：
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作者：
R. Roy;Derek G. Kamper;Xiaogang Hu
基于肌电的运动意图检测通常用于与辅助设备接口。然而，意图检测的性能对肌电信号的干扰很敏感。近年来，肌电信号被分解为运动单元（MU）的放电活动，神经元的二值放电活动可用于连续预测运动输出。实现了将MU发射映射到电机输出的不同函数，并且使用了复合MU发射频率和单个MU发射频率。目前尚不清楚某个映射功能是否优于其他功能。因此，我们评估了三种基于MU的手指动力学和运动学预测模型，通过改变MU的数量和将MU射击纳入回归模型的方法。我们还比较了具有不同通道数的三种基于肌电振幅的模型的性能。我们为实时实现提前执行了MU分解。结果表明，与使用所有微体的总体射击频率或一组具有相似射击活动的微体的总体射击频率作图方法相比，5个微体的单个射击频率与测量电机输出的相关性最高（力：0.86±0.01，关节角：0.93±0.01），估算误差最低（力：4.66±0.36% MVC，关节角：4.81±0.49°）。结果表明，种群水平的射击信息可能掩盖了单个MU射击的关键信息。这些发现使我们能够确定并发和连续手指力和关节角度估计的最佳模型。最小复杂性和高精度的结合使这些模型适合于辅助机器人设备的实时控制。
Myoelectric-based motor intent detection is typically used to interface with assistive devices. However, the intent detection performance is sensitive to interference of electromyogram (EMG) signals. Recently, EMG signals are decomposed into motor units (MU) firing activities, and neuron binary firing activities can be used to predict motor output in a continuous manner. Different functions that map MU firings to motor output have been implemented, and both composite MU firing frequency and individual MU firing frequency have been used. It is unclear whether one mapping function outperform others. Accordingly, we evaluated three MU-based finger kinetic and kinematic prediction models, by varying the number of MUs and the method of including MU firings into the regression model. We also compared the performance of three EMG amplitude-based models with varying number of channels. We performed MU decomposition in advance for real-time implementations. Our results showed that individual firing frequency of five MUs provided the lowest estimation error (force: 4.66±0.36 %MVC; joint angle: 4.81±0.49°) and highest correlation (force: 0.86±0.01; joint angle: 0.93±0.01) with the measured motor outputs, when compared with mapping method using the populational firing frequency of all MUs or the populational firing frequency of a group of MUs with similar firing activities. The results indicated that firing information at the population level may mask critical information of individual MU firings. These findings allowed us to identify the optimal models for concurrent and continuous finger force and joint angle estimation. A combination of the minimal level of complexity and high accuracy make these models suitable for real-time control of assistive robotic devices.