Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/app11104678
Keywords
temporal convolutional network; human-robot cooperation; surface electromyogram; continuous motion estimation
Categories
Funding
- National Natural Science and Foundation of China [NSFC 61902355, 61702493]
- Key-Area Research and Development Program of Guangdong Province [2019B010155003]
- Guangdong Basic and Applied Basic Research Foundation [2020B1515120044]
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The study introduces a novel method by applying temporal convolutional network (TCN) to sEMG-based continuous estimation, achieving a precision rate improvement in human-robot interface control.
Since continuous motion control can provide a more natural, fast and accurate man-machine interface than that of discrete motion control, it has been widely used in human-robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)-the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information-is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel's size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN's problem: that it is difficult to fully extract the sEMG's temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 x 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 x 3), LS-TCN (kernel size of 1 x 31) improves the precision rate by 6.6%.
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