Comparison of advanced control techniques for motion intention recognition using EMG signals

Gomez, Felipe; Villamarín, Diego Enrique Guzmán; Ruiz, William Alejandro; Prieto, Jeronimo Londono; Rodriguez, Juan P.

doi:10.1109/ccac.2017.8276481

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…The commonly used feature can be mainly divided into time domain feature, frequency domain feature, and timefrequency domain feature. For the time domain feature, mean absolute value (MAV) [27][28][29][30][31][32], root mean square (RMS) [29,31], variance (VAR) [29,31], standard deviation (SD) [29], zero count (ZC) [27,29,32], waveform length (WL) [27,29,32], slope sign change (SSC) [29,32], integrated EMG (IEMG) [33], and difference of mean absolute value (DMAV) [27] are commonly utilized. Although the calculation of time domain feature is simple, it is not enough to describe the information of signals.…”

Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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