The effect of prior walking on coronary heart disease risk markers in South Asian and European men.

Purpose: Heart disease risk is elevated in South Asians possibly due to impaired postprandial metabolism. Running has been shown to induce greater reductions in postprandial lipaemia in South Asian than European men but the effect of walking in South Asians is unknown. Methods: Fifteen South Asian and 14 White European men aged 19-30 years completed two, 2-d trials in a randomised crossover design. On day 1, participants rested (control) or walked for 60 min at approximately 50% maximum oxygen uptake (exercise). On day 2, participants rested and consumed two high fat meals over a 9h period during which 14 venous blood samples were collected. Results: South Asians exhibited higher postprandial triacylglycerol (geometric mean (95% confidence interval) 2.29(1.82 to 2.89) vs. 1.54(1.21 to 1.96) mmol·L-1·hr-1), glucose (5.49(5.21 to 5.79) vs. 5.05(4.78 to 5.33) mmol·L-1·hr-1), insulin (32.9(25.7 to 42.1) vs. 18.3(14.2 to 23.7) µU·mL-1·hr-1) and interleukin-6 (2.44(1.61 to 3.67) vs. 1.04(0.68 to 1.59) pg·mL-1·hr-1) than Europeans (all ES ≥ 0.72, P≤0.03). Between-group differences in triacylglycerol, glucose and insulin were not significant after controlling for age and percentage body fat. Walking reduced postprandial triacylglycerol (1.79(1.52 to 2.12) vs. 1.97(1.67 to 2.33) mmol·L-1·hr-1) and insulin (21.0(17.0 to 26.0) vs. 28.7(23.2 to 35.4) µU·mL-1·hr-1) (all ES ≥ 0.23. P≤0.01), but group differences were not significant. Conclusions: Healthy South Asians exhibited impaired postprandial metabolism compared with White Europeans, but these differences were diminished after controlling for potential confounders. The small-moderate reduction in postprandial triacylglycerol and insulin after brisk walking was not different between the ethnicities

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). © 2010 IEEE

Limitations and applications of ICA in facial sEMG and hand gesture sEMG for human computer interaction

In the recent past, there has been an increasing trend of using Blind Signal Separation (BSS) or Independent Component Analysis (ICA) algorithm for bio medical data, especially in prosthesis and Human Computer Interaction (HCI) applications. This paper reviews the concept of BSS and demonstrates its usefulness and limitations in the context of surface electromyogram related to hand movements and vowel classification. In the first experiment ICA has been used to separate the electrical activity from different hand gestures. The second part of our study considers separating electrical activity from facial muscles during vowel utterance. In both instances surface electromyogram has been used as an indicator of muscle activity. The theoretical analysis and experimental results demonstrate that ICA is suitable for identification of different hand gestures using SEMG signals. The results identify the unsuitability of ICA when the similar techniques are used for the facial muscles in order to perform different vowel classification. This technique could be used as a pre-requisite tool to measure the reliability of sEMG based systems in HCI. © 2007 IEEE

Spectral properties of surface EMG and muscle conduction velocity: A study based on sEMG model

It is well known that there is a change in the spectrum of surface electromyogram (sEMG) with the onset of muscle fatigue. This change has largely been attributed to the change in muscle conduction velocity. In this paper, this theory has been investigated by studying a sEMG model developed earlier. The change of spectrum of the experimental results has been compared with simulated sEMG in response to the change in muscle conduction velocity using similar conditions. The model of sEMG of biceps brachii muscle during various levels of isometric voluntary contraction was considered and included details of muscle type, measurable average and individual conduction velocities and the natural variations between motor unit recruitment and firing rates as observed experimentally. The model used the change in the muscle conduction velocity (v) as a parameter to simulate the sEMG. The shift in spectrum was identified by computation of the median frequency (MDF) of the sEMG before and after the implementation of the RS parameter in the model. The model was also simulated to generate sEMG at various levels of voluntary isometric contractions. The results based on the simulation of the model show that the rate of change in MDF was consistent in all levels of contractions and it is not same with experimental conditions. The results also suggest there is large change in MDF with v in the simulated sEMG than in experimental conditions. © 2011 IEEE

An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology

© 2001-2011 IEEE. Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence