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An LDA-based Approach for Real-Time Simultaneous Classification of Movements using Surface Electromyography.

IEEE Trans Neural Syst Rehabil Eng. 2019 Feb 22;:

Authors: Antuvan CW, Masia L

Myoelectric-based decoding strategies offer significant advantages in the areas of human-machine interactions because they are intuitive and requires less cognitive effort from the users. However, a general drawback in using machine learning techniques for classification is that the decoder is limited to predicting only one movement at any instant, hence restricted to performing the motion in a sequential manner. Whereas, human motor control strategy involves simultaneous actuation of multiple degree-of-freedoms (DOFs), and is considered to be a natural and efficient way of performing tasks. Simultaneous decoding in the context of myoelectric-based movement control is a challenge that is being addressed recently and is increasingly popular. In this paper, we propose a novel classification strategy capable of decoding both individual and combined movements, by collecting data from only the individual motions. Additionally, we exploit low dimensional representation of the myoelelectric signals using a supervised decomposition algorithm called linear discriminant analysis, to simplify the complexity of control and reduce computational cost. The performance of the decoding algorithm is tested in an online context for a two degreeof- freedom task comprising the hand and wrist movements. Results indicate a overall classification accuracy 88:02% for both individual and combined motions.

PMID: 30802866 [PubMed – as supplied by publisher]

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