A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Abstract

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.

An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition

“…One straightforward solution is system recalibration; however, a frequent recalibration procedure is time-consuming which makes it impractical for the clinical use of prosthetics. Several other advanced solutions have also been explored, such as the selection of robust EMG features [ 20 , 22 ], special training strategies [ 23 ], sensor fault detection [ 24 ], and adaptive pattern recognition strategies [ 25 , 26 , 27 , 28 , 29 , 30 ].…”

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…Typically, HD-sEMG employs a large two-dimensional (2D) array of closely spaced electrodes with small size. The total number of electrodes that has been proposed for HD-sEMG is in the range of 32 [12] to over 350 [42], while the maximum number of electrodes for typical EMG armbands is 16 (Tables 1 and 2). The existing shared HD-sEMG data sets, which use electrode arrays of 32, 128, and 192, are listed in Table 3.…”

“…Often, the active region of the HD-sEMG map associated with a certain muscle, the so-called activation map, is identified using an image segmentation method and used as an input for subsequent feature extraction methods. Features extracted from HD-sEMG maps can be based on intensity information (any signal magnitude and power feature [18,22]) and spatial information (e.g., the mean shift [42] or the coordinates of the centre of gravity and maximum values [44]). These maps and additional spatial-based features can be used to reduce the effect of confounding factors that influence the performance of EMG pattern recognition such as the changing characteristics of the signal itself over time and electrode location shift [45] as well as variations in muscle contraction intensity [44].…”

“…Two methods of analyzing these kinds of EMG signals include the HD-sEMG map (a topographical image) and the sEMG image (an instantaneous image). Following conventional EMG pattern recognition methods, the HD-sEMG map can be computed using the root mean square (RMS) [42] or other amplitude-based feature extraction methods (e.g., MAV, WL, etc.) [43], of individual channels distributed in 2D space.…”

Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue

“…Electromyography is a method that evaluates, through non-invasive techniques, the overall behavior of the muscle fibers that make up the muscle [1]. Electromyographic signals are those that are produced by a muscle during the process of contraction and relaxation.…”