Motion Intention Estimation of Finger Motions with Spatial Variations of HD EMG Signals
Motion Intention Estimation of Finger Motions with Spatial Variations of HD EMG Signals
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DOI：
10.1109/iccae56788.2023.10111336
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
D. Bandara;He Chongzaijiao;J. Arata
中科院分区：
文献类型：
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作者：
D. Bandara;He Chongzaijiao;J. Arata
人手运动意图的估计在机器人和其他以人为中心的领域有着广泛的应用。特别是在可穿戴机器人应用中，基于生物信号的人手运动估计被广泛使用。然而，基于用于手指运动的肌肉的构造，以及人手的独立运动的更高数量，准确且有效地估计手指运动仍然是当前技术的挑战。另一方面，高密度肌电图（HDEMG）具有在其测量下提供肌肉群的高分辨率空间激活图像的能力。在这项研究中，HDEMG信号被用来估计手指运动，通过使用不同的手指运动过程中的表面HDEMG信号的空间变化。因此，特征的Gabor滤波器和纠错输出编码的方法被用来分类手指运动的六个运动类。结果表明，该方法可以成功地分类的运动与更高的精度，使用HDEMG数据中包含的空间信息。
Estimation of motion intention of human hand has many applications in robotics and other human centred areas. Especially with wearable robotic applications biosignal based estimation of human hand motions are widely used. However, based on the construction of the muscles for finger motions, and the higher number of independent motions of the human hand, estimation of finger motions accurately and effectively remains a challenge with current techniques. On the other hand, high density electromyography (HDEMG), has the capability to provide a high resolution spatial activation image of the muscle group under its measurement. In this study HDEMG signals were used to estimate the finger motions, by using the spatial variations of the surface HDEMG signals during the different finger motions. Thus, features of Gabor filters and error-correcting output codes method was used to classify six motion classes of finger motions. Results showed the proposed methodology can successfully classify the motions with a higher accuracy, using the spatial information contained in HDEMG data.