Time series modeling of surface EMG based hand manipulation identification via expectation maximization algorithm

Abstract

a b s t r a c tIn this paper, we focus on the method of employing the expectation maximization (EM) algorithm to the modeling of surface electromyography (sEMG) signals based on hand manipulations via available time series of the measured data. The model for the sEMG is developed as a hidden Markov model (HMM) framework. In order to represent dynamical characteristics of sEMG when multichannel observation sequence are given, a stochastic dynamic process is included in it based on the maximum likelihood estimation (MLE) principle. By using the EM algorithm, the hidden model parameters and the feature of the signal can be identified easily. Ten people of different time series data sets of different hand grasps and in-hand manipulations captured from different subjects are collected. The two different classifiers were used to recognize these hand manipulation signal. Compared with time and time-frequency domains and their combination feature, the proposed algorithm of the inferred model gains better performance and demonstrates the effectiveness. The average identification accuracy rate is 93% and the maximum classification ratio is 100%.

Electromechanical Design of Self‐Similar Inspired Surface Electrodes for Human‐Machine Interaction

“…Capacitance C E determines SNR through its effect on the gain of the preamplifier. As expected, the output gain G is also improved as the C E is increased with increasing effective area of the electrodes [41]. Figure 10 Figure 10(c) depicts the skin-electrode interface contact impedance decreases with the increasing scanning frequency with the area 24 mm 2 and 40 mm 2 .…”

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

“…The studied pattern recognition of the hand gesture is summarized in Table 1. The utilized number of channels in sEMG varies from two to 16 channels [6][7][8][9][10][12][13][14]. The selected sampling rate for the hand gesture recognition varied from 1 kHz to 4 kHz.…”

“…The selected sampling rate for the hand gesture recognition varied from 1 kHz to 4 kHz. Lu et al [12] studied ten hand gesture motions with 16 channels of the sEMG sensor. The acquired sEMG signals were processed using seven features.…”

“…The studies presented in Table 1 show that the support vector machine (SVM), k-nearest neighbors (KNN), and artificial neural network (ANN) have been commonly used for EMG pattern recognition as classifiers. Most of the previous research employed sEMG signals with more than one channel [6][7][8][9][10][11][12][13][14]. The range of the sample frequency of the sEMG data acquisition was from 1 kHz to 4 kHz.…”

SEMG based Intention Identification of Complex Hand Motion using Nonlinear Time Series Analysis

“…Ju et al [6] proposed non-linear feature extraction and classification method to evaluate different hand motions, and concluded that recurrence plot (RP) and Fuzzy Gaussian Mixture Models (FGMMs) as nonlinear analysis methods are suitable for dynamical characteristics representation of SEMG signal. Lu et al [7] used hidden Markov model (HMM) and Expectation Maximization (EM) algorithms to recognize ten hand manipulation signals. Xiaochuan Yin and Qijun Chen [8] presented a nonlinear time alignment method with deep autoencoder to extract spatio-temporal features for human action recognition.…”