Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.

中文翻译：

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肌肉协同功能的高斯过程模型，用于使用测得的子集估算未测的肌肉兴奋

从减少的传感器阵列估计肌肉兴奋度可以大大改善当前的远程患者监测技术。这种方法可以允许连续监测临床上相关的生物力学变量，这些变量对于个性化康复非常理想。在本文中，我们介绍了肌肉协同功能的概念，该功能描述了肌肉子集之间的协同关系。我们使用高斯过程回归从第一原理开发出近似的行为，并演示了该技术在九名健康受试者正常行走过程中估算腿部肌肉兴奋时间序列的实用性。特别，使用表面肌电图（sEMG）数据在有限的时间间隔（称为输入窗口）中从四个不同的肌肉（称为输入肌肉）估计了六块肌肉的兴奋，平均绝对误差（MAE）小于最大自愿收缩的5.0％ （MVC），它占真实激励中方差（VAF）的82-88％。此外，这些估计的兴奋以少于4.0％的MAE和89-93％的VAF通知了肌肉激活。我们还对许多不同的建模选择进行了详细分析，包括四，三和两个肌肉输入集的每种可能组合，输入窗口的大小和结构以及高斯过程协方差函数的平稳性。此外，讨论了针对特定应用的修改以供将来使用。