Sommer L F, Barreira C, Noriega C, Camargo-Junior F, Moura R T, Forner-Cordero A
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1472-1475. doi: 10.1109/EMBC.2018.8512512.
This paper presents a method to estimate the elbow joint angle from surface electromyography (sEMG) measurements of biceps, triceps and brachioradialis. This estimation is of major importance for the design of human robot interfaces based on sEMG. It is also relevant to model the muscular system and to design biomimetic mechanisms. However, the processing and interpretation of electromyographic signals is challenging due to nonlinearities, unmodeled muscle dynamics, noise and interferences. In order to determine an estimation model and a calibration procedure for the model parameters, a set of experiments were carried out with six subjects. The experiments consisted of series of continuous (cyclical) and discrete elbow flexo-extensions with three different loads (i.e. 0 kg, 1.5kg and 3 kg). The sEMG data from the biceps brachii, triceps brachii and brachioradialis and the joint angle were recorded. Four different modeling techniques were evaluated: State Space (SS), Autoregressive with Exogenous Input (ARX), Autoregressive Moving-Average with Exogenous Input (ARMAX), Autoregressive Integrated Moving-Average with Exogenous Input (ARIMAX). After the model was selected, a second experiment was performed in order to validate the estimation procedure. The results show a procedure to estimate the EMG-to-angle relation with high correlation and low meansquare- root errors with respect to the measured angle data.
本文提出了一种根据肱二头肌、肱三头肌和桡侧腕长伸肌的表面肌电图（sEMG）测量值来估计肘关节角度的方法。这种估计对于基于sEMG的人机接口设计至关重要。它对于肌肉系统建模和仿生机制设计也具有重要意义。然而，由于非线性、未建模的肌肉动力学、噪声和干扰，肌电信号的处理和解释具有挑战性。为了确定估计模型和模型参数的校准程序，对六名受试者进行了一组实验。实验包括一系列连续（循环）和离散的肘关节屈伸动作，施加三种不同的负荷（即0千克、1.5千克和3千克）。记录了肱二头肌、肱三头肌和桡侧腕长伸肌的sEMG数据以及关节角度。评估了四种不同的建模技术：状态空间（SS）、带外生输入的自回归（ARX）、带外生输入的自回归移动平均（ARMAX）、带外生输入的自回归积分移动平均（ARIMAX）。选择模型后，进行了第二个实验以验证估计程序。结果显示了一种估计肌电与角度关系的程序，该程序与测量角度数据具有高相关性和低均方根误差。
