Malesevic Nebojsa, Markovic Dimitrije, Kanitz Gunter, Controzzi Marco, Cipriani Christian, Antfolk Christian
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1518-1523. doi: 10.1109/ICORR.2017.8009463.
In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.
在本文中，我们提出了一种从表面肌电图（EMG）预测单个手指运动的新方法。该方法旨在对多功能假肢装置进行实时灵巧控制。使用位于健康参与者前臂上的16个单端通道记录肌电图数据。在记录肌电图的同时，受试者根据视觉提示进行连续的手指运动。我们的算法可按以下步骤描述：提取肌电图的平均绝对值（MAV）作为分类特征，对肌电图特征动态进行分段线性建模，实施分层隐马尔可夫模型（HHMM）以捕捉线性模型之间的转换，以及实施贝叶斯推理作为分类器。我们将分类器的性能与常用的实时分类器进行了评估。结果表明，当前的算法设置对肌电图数据的分类与测试分类器中最佳的分类相似，但计算复杂度相同或更低。
