Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements

EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem. The EMG pattern recognition mainly consists of four stages; signal detection and preprocessing feature extraction, dimensionality reduction and classification. However, the success of any pattern recognition technique depends on the feature extraction and dimensionality reduction stages. In this paper time domain (TD) with 6th order auto regressive (AR) coefficients features and three techniques of dimensionality reduction; principal component analysis (PCA), uncorrelated linear discriminant analysis (ULDA) and fuzzy neighborhood preserving analysis with QR decomposition (FNPA-QR) were demonstrated. The EMG data were recorded from the below knee muscles of ten intact-subjects. Four ankle joint movements are classified using three classifiers; LDA, k-NN and MLP. The results show the superiority of TD&6th AR with FNPA-QR and k-NN combination with (96.20% ± 4.1) accuracy

Review of surface electrode placement for recording electromyography signals

Background: Surface Electromyography (SEMG) signal has used in monitoring muscle activities. It has been widely applied in many areas, such as body member prosthesis, noise cancellation for brain-computer interface, and robotics. The SEMG acquisition method for collecting the signal with low-noise has extensively investigated in the last decade. The objective of this study is to review the recent works on electrode position and identify avenues for future research. Methods: A review of the relevant literature published between 1986 and 2015. This study commences with the basics of SEMG and recent methods for electrode position. Result: The different noises affecting SEMG signal include the spread of the innervation zone, cross-talk from neighbour muscles, electrode size, and location of electrode placement. Moreover, electrode placement or displacement effect SEMG signal in both time and frequency domain. Conclusion: Although several SEMG studies examined the effects of electrode position and internal electrode distance on forearm muscles, only a few studies addressed the methodological difficulties of the electrode position. In the majority of studies, electrodes were placed without the specific symptoms of the points along the length or shape of the muscle. Moreover, IED varied in different studies

Modelling and control of standing up and sitting down manoeuver

Exoskeleton Robot is one of the most significant examples of human-oriented robotic devices. Nevertheless, the main challenge remains the complexity of their mechanical design and human-robot interfaces. This paper is an outcome of a research to model and to simulate the support of mobility of an elderly people using exoskeleton. Exoskeleton is developed in order to complement the corporal deficiencies of an elderly person in standing up and sitting down. When the natural joint torques is integrated with the exoskeleton's torque the result is in an overall torque that is comparable to that of a physically normal person. This work focuses on standing-up and sitting-down movements. Appropriate simulation models are formulated and their performances examined against measured data. The results with PID control show that at different speed of standing up and sitting down, the joint torques can be compromised. This is done within allowable limits

Classification of ankle joint movements based on surface electromyography signals

Electromyography (EMG) signal has valuable information about the force of the muscle contraction and the movement direction. This crucial information has been used for many years in exoskeleton, orthoses and prostheses robots. An essential part of those devices is EMG based control system that employs the EMG signal from different muscles to control prostheses and exoskeleton robot. However, using EMG signal as an input control signal for those devices is not easy due to the complexity nature of this signal that produces the different body movements. This difficulty can be overcome by using pattern recognition techniques to discriminant different limb movement’s pattern then use the classified signal as input control signal to manipulate and drive the assistive robot devices. Though much research have been carried out to classify the upper and lower limbs movement based on the EMG signal, still there is a strong need to obtain an accurate pattern classification system in computationally efficient manner. In this work two parts are primarily presented. The first partt was design and implements a multichannel EMG acquisition system to detect and acquire the leg muscles’ signal. In this part four EMG channels were implemented using instrumentation amplifier (INA114) for pre-amplification stage then the amplified signal was filtered using band pass filter to eliminate the unwanted signals. Operational amplifier (OPA2604) was involved for the main amplification stage to get the output signal in volts. The EMG signals were detected during movement of the ankle joint of a healthy subjects. Then the signal sampled at rate of 2 kHz using NI 6009 DAQ card and LabVIEW software was employed to store and display the acquired signal. Fast Fourier Transform (FFT) and Signal to Noise Ratio (SNR) were applied to assess the recoded electromyography signal. The second part is to classify four ankle joint movements which are dorsiflexion, plantar flexion, adduction and abduction. The data was collected from twenty healthy subjects using the custom multichannel EMG acquisition system designed in the first part of this project. In this section, new time domain feature set was evaluated and compared with well known time domain features. Three classifiers were employed to evaluate the two feature sets. These classifiers are linear discriminant Analysis (LDA), K nearest neighbourhood (k-NN) and Naïve Bayes classifier (NB). The result showed the superiority of the new time domain feature set which are the logarithmic based time domain features upon the conventional time domain feature. In addition, the results show the outperformance of LDA classifier among the other two classifiers used in this study