Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People

Abstract

<b>Problem statement:</b> The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To improve the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have been trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, organ, or cell system like the nervous system are the driving force for the entire system. Examples of biosignals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). <b>Approach:</b> Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacing system enables the physically disabled to use word processing programs, other personal computer software and internet. <b>Results:</b> Depending on the application, the acquired and processed signals need to be classified for interpreting into mechanical force or machine/computer command. <b>Conclusion:</b> This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility and applications. This review will be beneficial to the EMG signal researchers as a reference and comparison study of EMG classifier. For the development of robust, flexible and efficient applications, this study opened a pathway to the researchers in performing future comparative studies between different EMG classification methods

Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

“…Artificial Neural Networks (ANN) have emerged as a significant and efficient tool for analyzing complex data and pattern classification. The ANNs are formed by mimicking the low-level tasks of biological neurons which make them particularly useful for recognizing and classifying complex patterns [4]. Some of the pioneer research work for EMG signal classification task can be mentioned herewith: integral absolute value (IAV) feature based feed-forward ANN [5], AR parameter based ANN [6], independent component analysis (ICA) based ANN [7], different multi-layer perceptron (MLP) based neural network [8]- [10], Hopfield and adaptive resonance theory (ART) based neural network, and later finite impulse response neural network (FIRNN) [11], and linear vector quantization (LVQ) type ANN [12].…”

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

“…There are many efforts in fuzzy approach (Ajiboye and Weir, 2005) and Evidence Accumulation (EA) method was applied by Gazzoni et al, (2004). Neuro-Fuzzy approach has also been used for EMG classification especially in machine control fields (Kiguchi et al, 2003;Ahsan et al, 2010). Then, FCNN, simplified fuzzy ARTMAP and FMMNN were introduced by and Han et al (2004) respectively.…”

Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal

“…Also the power spectral density of the denoised signal is plotted. From the observations the performance of both adaptive filter and the Neuro-fuzzy filter is noted (Ahsan et al, 2010). By comparative study we thereby conclude that the performance of Neuro-Fuzzy filter is better than the Adaptive filter.…”

“…Croft and Barry (2006) reviews a number of methods of dealing with ocular artifacts in EEG, focusing on the relative merits of a variety of EOG correction procedures (Ahsan et al, 2010). Describes the basic concepts of wavelet analysis and other applications.…”