基于超球体模型利用肌电信号的运动辨别技术
Motion discrimination technique by EMG signals using hyper-sphere model.
作者信息
Yamamoto Tetsushi, Tsujiuchi Nobutaka, Ito Akihito, Koizumi Takayuki
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:5-8. doi: 10.1109/EMBC.2014.6943515.
This study developed a method of discriminating real-time motion from electromyogram (EMG) signals. We previously proposed a real-time motion discrimination method using hyper-sphere models that discriminated five motions (open, grasp, pinching, wrist extension, and wrist flexion) above 90% and quickly learned EMG signals. Our method prevents elbow motions from interfering with hand motion discrimination. However, we presume in our method that feature quantities do not change with time. Discrimination accuracy might deteriorate over time. Additionally, our method only discriminated three motions (open, grasp, pinching) for finger motions. This paper proposes the effectiveness of our method for changing feature quantities caused by time variation and a real-time motion discrimination method using new hyper-sphere models for four finger motions (open, grasp, pinching, and 2-5th finger flexion). We carried out two experiments and verified the effectiveness of our method for changing feature quantities and four finger motions discrimination using the new hyper-sphere models.
本研究开发了一种从肌电图（EMG）信号中辨别实时运动的方法。我们之前提出了一种使用超球体模型的实时运动辨别方法，该方法对五种运动（张开、抓握、捏、腕伸展和腕屈曲）的辨别准确率高于90%，并且能快速学习EMG信号。我们的方法可防止肘部运动干扰手部运动辨别。然而，我们的方法假定特征量不随时间变化。随着时间推移，辨别准确率可能会下降。此外，我们的方法仅针对手指运动辨别三种运动（张开、抓握、捏）。本文提出了我们的方法对于由时间变化引起的特征量变化的有效性，以及一种使用新的超球体模型针对四种手指运动（张开、抓握、捏和第2 - 5指屈曲）的实时运动辨别方法。我们进行了两项实验，并验证了我们的方法对于特征量变化以及使用新超球体模型进行四种手指运动辨别的有效性。
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