2020
DOI: 10.1007/s13246-020-00868-1
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Crosstalk in surface electromyogram: literature review and some insights

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Cited by 39 publications
(21 citation statements)
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“…Although this peak is the most prominent and consistent in all gait data, it is considered to be the crosstalk from vastus lateralis [ 29 , 30 ]. Crosstalk is a known limitation of the SEMG and is widely reported in the literature [ 31 ]. Since NN relies on the data to produce correct output responses, this crosstalk will always be present in the predicted results.…”
Section: Discussionmentioning
confidence: 99%
“…Although this peak is the most prominent and consistent in all gait data, it is considered to be the crosstalk from vastus lateralis [ 29 , 30 ]. Crosstalk is a known limitation of the SEMG and is widely reported in the literature [ 31 ]. Since NN relies on the data to produce correct output responses, this crosstalk will always be present in the predicted results.…”
Section: Discussionmentioning
confidence: 99%
“…For the more involved and higher DoF movements, such as that of the hand, a machine learning-based myoelectric muscle motion pattern recognition has proven to be a reliable method due to its characteristics previously detailed in Section 3.3.2 [ 54 ]. In addition, an EMG signal is usually not limited to the data generated by one muscle, and could also be reflecting the activity of the neighboring muscles in the area, the so-called crosstalk issue [ 106 ]. Therefore, the fact that different muscles are associated with different motions leads to difficulty in the unequivocal association of the EMG signal with motion.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, the muscle force estimation would involve errors. Even though the EMG-informed method helped to account for the cocontraction of quadriceps and hamstrings, 36 surface EMG information would contain errors from crosstalk 33 ; in addition, the estimation was not calibrated to be specific for patients as conducted in a previous study. 22,40 Uncoupled modeling between OpenSim and the FE model in this study would be another factor for the potential errors in the estimation of muscle force and subsequent ACL loads.…”
Section: Discussionmentioning
confidence: 99%