Independence Between Two Channels of Surface Electromyogram Signal to Measure the Loss of Motor Units

This study has investigated the relationship in the connectivity of motor units in surface electromyogram (sEMG) of biceps brachii muscle. It is hypothesized that with ageing, there is reduction/loss in number of motor units, leading to reduction in the independence between the channels of the recorded muscle activity. Two channels of sEMG were recorded during three levels of isometric muscle contraction: 50 %, 75 % and 100 % maximal voluntary contraction (MVC). 73 subjects (age range 20-70) participated in the experiments. The independence in channel index (ICI) between the two sEMG recording locations was computed using the independent components and Frobenius norm. ANOVA Statistical analysis was performed to test the effect of age (loss of motor units) and level of contraction on ICI. The results show that the ICI among the older cohort was significantly lower compared with the younger adults. This research study has shown that the reduction in number of motor units is reflected by the reduction in the ICI of the sEMG signal

ICA as pattern recognition technique for gesture identification : a study using bio-signal

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user

Independent component analysis for classification of surface electromyography signals during different MVCs

The existence of cross-talk and noise from narrowly located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification of sEMG. This research reports a new classification technique for sEMG using Blind Source Separation Techniques (BSS) such as Independent Component Analysis (ICA). The technique uses BSS methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that BSS methods are useful indicator of muscle properties and are a useful indicator of the level of muscle activity

Independence between two channels of surface electromyogram signal to measure the loss of motor units

This study has investigated the relationship in the connectivity of motor units in surface electromyogram (sEMG) of biceps brachii muscle. It is hypothesized that with ageing, there is reduction/loss in number of motor units, leading to reduction in the independence between the channels of the recorded muscle activity. Two channels of sEMG were recorded during three levels of isometric muscle contraction: 50 %, 75 % and 100 % maximal voluntary contraction (MVC). 73 subjects (age range 20-70) participated in the experiments. The independence in channel index (ICI) between the two sEMG recording locations was computed using the independent components and Frobenius norm. ANOVA Statistical analysis was performed to test the effect of age (loss of motor units) and level of contraction on ICI. The results show that the ICI among the older cohort was significantly lower compared with the younger adults. This research study has shown that the reduction in number of motor units is reflected by the reduction in the ICI of the sEMG signal

Towards classification of low-level finger movements using forearm muscle activation : a comparative study based on ICA and Fractal theory

There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA

A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines

Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors.These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF)

LoCoMo-Net : a low -complex deep learning framework for sEMG-based hand movement recognition for prosthetic control

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons

Limitations and applications of ICA for surface electromyogram for identifying hand gestures

Surface electromyogram (SEMG) has numerous applications, but the presence of artifacts and cross talk especially at low level of muscle activity makes the recordings unreliable. Spectral and temporal overlap can make the removal of artifacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Identification of hand gestures using low level of SEMG is one application that has a number of applications but the presence of high level of cross talk makes such an application highly unreliable. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, a number of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artifacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and a number of sources. This paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. This paper also proposes semi-blind ICA approach with the combination of prior knowledge of SEMG sources with ICA to identify hand gestures using low level of SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. This paper determines the suitability of the use of error between estimated and actual mixing matrix as a mean for identifying the quality of separation of the output. This work also demonstrates that semi-blind ICA can accurately identify complex hand gestures from the low-level SEMG recordings