Castro Maria Claudia F, Colombini Esther L, Aquino Plinio T, Arjunan Sridhar P, Kumar Dinesh K
Electrical Engineering Department, Centro Universitário da FEI, Av, Humberto de A, C, Branco, 3,972, São Bernardo do Campo, SP 09850-901, Brazil.
Biomed Eng Online. 2014 Nov 25;13:155. doi: 10.1186/1475-925X-13-155.
Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.
从表面肌电图（sEMG）自动准确识别肘部角度对于肌电控制的上肢外骨骼系统至关重要。这需要适当选择sEMG特征，并识别该系统的局限性。本研究表明，仅使用200毫秒的窗口大小就有可能识别肘部的三个离散位置：完全伸展、直角和中点。可以看出，虽然大多数特征适用于此目的，但功率谱密度平均值（PSD-Av）表现最佳。当仅考虑两个离散位置（完全伸展和肘部呈直角）时，该系统针对肘部角度对sEMG的正确分类率为100%，而当有三个离散位置时，正确分类率为89%。然而，当考虑五个离散角度时，sEMG无法准确确定肘部位置。还观察到伸展或屈曲阶段没有差异。
