Sridhar Poosapadi Arjunan

Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors.

BackgroundIdentifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG) can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI) devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings.MethodsSEMG signal was recorded when the participant maintained pre-specified wrist and finger flexion movements for a period of time. Various established sEMG signal parameters such as root mean square (RMS), Mean absolute value (MAV), Variance (VAR) and Waveform length (WL) and the proposed fractal features: fractal dimension (FD) and maximum fractal length (MFL) were computed. Multi-variant analysis of variance (MANOVA) was conducted to determine the p value, indicative of the significance of the relationships between each of these parameters with the wrist and finger flexions. Classification accuracy was also computed using the trained artificial neural network (ANN) classifier to decode the desired subtle movements.ResultsThe results indicate that the p value for the proposed feature set consisting of FD and MFL of single channel sEMG was 0.0001 while that of various combinations of the five established features ranged between 0.009 - 0.0172. From the accuracy of classification by the ANN, the average accuracy in identifying the wrist and finger flexions using the proposed feature set of single channel sEMG was 90%, while the average accuracy when using a combination of other features ranged between 58% and 73%.ConclusionsThe results show that the MFL and FD of a single channel sEMG recorded from the forearm can be used to accurately identify a set of finger and wrist flexions even when the muscle activity is very weak. A comparison with other features demonstrates that this feature set offers a dramatic improvement in the accuracy of identification of the wrist and finger movements. It is proposed that such a system could be used to control a prosthetic hand or for a human computer interface.

Visual Speech Recognition Using Image Moments and Multiresolution Wavelet Images

This paper describes a new technique for recognizing speech using visual speech information. The video data of the speakers mouth is represented using grayscale images named as motion history image (MHI). MHI is generated by applying accumulative image differencing on the frames of the video to implicitly represent the temporal information of the mouth movement. The MHIs are decomposed into wavelet sub images using discrete stationary wavelet transform (SWT). Three moment-based features (geometric moments, Zernike moments and Hu moments) are extracted from the SWT approximate sub images. Multilayer perceptron (MLP) type artificial neural network (ANN) with back propagation learning algorithm is used to classify the moments features. This paper evaluates and compares the image representation ability of the different moments. The initial experiments show that this method can classify English consonants with an error rate less than 5%

Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques

Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques The presence of noise and cross-talk from closely 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. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (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 ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

Evaluation of feature extraction techniques in emotional state recognition

We present in this paper a study of three EEG signals feature extraction techniques. These techniques have been widely employed in researches of emotional states recognition: statistical characteristics, features based on PSD (Power Spectral Density) and features based on HOC (High Order Crossings). The validation was performed via classification of emotional states of calm and stress using the K-NN based classifier in off-line mode using EEG signals from available DEAP database. The best results achieved were 70.1%, using the PSD based technique, and 69.59% using the HOC based technique.

Measuring Increase in Synchronization to Identify Muscle Endurance Limit

Changes in surface electromyogram (sEMG) spectral content are commonly associated with localized muscle fatigue. However, the significance of the changes is only evident during pair-wise comparison and these can only be used for comparison between the rested and fatigued muscle and cannot be used for identifying the limit of muscle endurance without having the rested data for comparison. This is due to the large variations between sEMG at different levels of strengths of contraction, and between different people. This is further compounded when the contraction is not isometric but is cyclic because there is large variation of sEMG within each cycle. This research has developed a new sEMG based method for studying muscle fatigue and for identifying the limit of muscle endurance. It is based on motor unit synchronization and is called increase in synchronization (IIS) index. IIS index measures the level of independence between two channels of sEMG recorded from the muscle and is the log of the determinant of the global matrix ( log||G||) which is generated by performing independent component analysis on the two channels. The experimental results for biceps brachii demonstrate that when the muscle was rested, the two channels had a high degree of independence and the IIS index was greater than -0.7 (range -0.65 to -0.05). However, the channels became dependent as the muscles progressively fatigued and IIS index became less than -6.2 (range -7.8 to -6.3 ) at the limit of muscle endurance. This was irrespective of the contraction being isometric or cyclic, or of the level of muscle contraction.

Fractal Based Modelling and Analysis of Electromyography (EMG) To Identify Subtle Actions

The paper reports the use of fractal theory and fractal dimension to study the non-linear properties of surface electromyogram (sEMG) and to use these properties to classify subtle hand actions. The paper reports identifying a new feature of the fractal dimension, the bias that has been found to be useful in modelling the muscle activity and of sEMG. Experimental results demonstrate that the feature set consisting of bias values and fractal dimension of the recordings is suitable for classification of sEMG against the different hand gestures. The scatter plots demonstrate the presence of simple relationships of these features against the four hand gestures. The results indicate that there is small inter-experimental variation but large inter-subject variation. This may be due to differences in the size and shape of muscles for different subjects. The possible applications of this research include use in developing prosthetic hands, controlling machines and computers.

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05).

Unspoken vowel recognition using facial electromyogram.

The paper aims to identify speech using the facial muscle activity without the audio signals. The paper presents an effective technique that measures the relative muscle activity of the articulatory muscles. Five English vowels were used as recognition variables. This paper reports using moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles to segment the signal and identify the start and end of the utterance. The RMS of the signal between the start and end markers was integrated and normalised. This represented the relative muscle activity of the four muscles. These were classified using back propagation neural network to identify the speech. The technique was successfully used to classify 5 vowels into three classes and was not sensitive to the variation in speed and the style of speaking of the different subjects. The results also show that this technique was suitable for classifying the 5 vowels into 5 classes when trained for each of the subjects. It is suggested that such a technology may be used for the user to give simple unvoiced commands when trained for the specific user

Selection of suitable hand gestures for reliable myoelectric human computer interface

BackgroundMyoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity.MethodsExperiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive–Negative Performance Measurement Index (PNM), generated by a series of confusion matrices.ResultsWhen using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion.ConclusionThis work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.