A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition

Abstract

The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only.

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

“…Scheme and Englehart [21] re-evaluated the performance of the commonly used Hudgins' time domain features (ZC, SSC, mean absolute value (MAV) and waveform length (WL) [28]) and several additional features (autoregressive coefficients, AR; cepstral; coefficients, CC; Willison amplitude, WAMP; and sample entropy, SampEn) using six different EMG data sets containing over 60 subject sessions and 2500 separate contractions. Khushaba et al [29] proposed a novel set of time domain features that can estimate the EMG signal power spectrum characteristics using five different EMG data sets. Phinyomark et al [26,30] investigated the effect of sampling rate on EMG pattern recognition and then identified a novel set of features that are more accurate and robust for emerging low-sampling rate EMG systems, using four different EMG data sets containing 40 subject sessions with over 8000 separate contractions.…”

“…Feature extraction, which transforms short time windows of the raw EMG signal to generate additional information and improve information density, is thus required before a classification output can be computed. During the past several decades, numerous different EMG feature extraction methods based on time domain, frequency domain, and time-frequency domain information have been proposed and explored [7,8,[18][19][20]22,26,[28][29][30]. Interesting EMG feature extraction methods include a set of ZC, SSC, MAV, and WL (the most commonly used features [28]); AR and CC (the robust features for EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue [8]); WAMP (a robust feature against noise [64,65]); SampEn (a robust feature against between-day variability [7]); and L-scale (an optimal feature for wearable EMG devices [26]), to name a few.…”

“…Interesting EMG feature extraction methods include a set of ZC, SSC, MAV, and WL (the most commonly used features [28]); AR and CC (the robust features for EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue [8]); WAMP (a robust feature against noise [64,65]); SampEn (a robust feature against between-day variability [7]); and L-scale (an optimal feature for wearable EMG devices [26]), to name a few. For extended coverage of window-based EMG feature extraction methods, the reader is encourage to consult some of these aforementioned studies [7,8,[18][19][20]22,26,[28][29][30].…”

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

“…Increasing the number of the input signals also enables advanced data processing and classification techniques. Indeed, computational methods such as support vector regression [16], tree-structured neural network [17], Bayesian inference [18], ICA clustering [19], hidden Markov models [20], nonnegative matrix factorization [21], and various pattern recognition approaches [22][23][24][25], demonstrated promising classification results while using multiple discrete EMG channels or high-density surface EMG electrodes.…”

Multi-grip classification-based prosthesis control with two EMG-IMU sensors

“…Several studies have also used this paradigm to decode grips and gestures for intuitive prosthetic hand control. In their majority, however, they have been either limited to offline analyses 6-8 or only included able-bodied participants 9-11 , with few exceptions demonstrating real-time control with amputees 12, 13 .One caveat of classification-based myoelectric control is the requirement for a relatively large number of sensors 14, 15 or high-density electrode arrays 16,17 . This requirement increases the overall complexity and cost of the system and reduces its practicality, due to increased weight and the burden associated with a constant need for positioning a large number of electrodes.…”