Ravinder Agarwal

Interpretations of Wrist/Grip Operations From SEMG Signals at Different Locations on Arm

Surface electromyogram (SEMG) is a common method of measurement of muscle activity. It is noninvasive and is measured with minimal risk to the subject. The analysis of SEMG signal depends on a number of factors, such as amplitude as well as time- and frequency-domain properties. In the present investigation, the study of SEMG signals at different below elbow muscles for four operations of the hand wrist/grip-like opening (op)/closing (cl)/down (d)/up (u) was carried out. Myoelectric signals were extracted by using a single-channel SEMG amplifier consisting of a differential amplifier, noninverting amplifier, and interface module. Matlab softscope was used to acquire the SEMG signal from the hardware. After acquiring the data from six selected locations, interpretations were made for the estimation of parameters of the SEMG using the Matlab-filter algorithm and the fast Fourier transform technique. An interpretation of wrist/grip operations using principal component analysis (PCA) was carried out. PCA was used to identify the best SEMG signal capturing system out of two-channel, three-channel, and four-channel systems. Two acupressure points (on wrist) were also selected for the analysis with other points on the arm. SEMG signals study at different locations, including pressure points, will be a very helpful tool for the researchers in understanding the behavior of SEMG for the development of the prosthetic hand.

Effects of agriculture crop residue burning on children and young on PFTs in North West India.

Variations in pulmonary function tests (PFTs) due to agriculture crop residue burning (ACRB) on children between the age group of 10 to 13 years and the young between 20 to 35 years are studied. The effects of exposure to smoke due to rice-wheat crop residue burning on pulmonary functions like Force Vital Capacity (FVC), Force Expiratory Volume in one second (FEV(1)), Peak Expiratory Flow (PEF) and Force Expiratory Flow in 25 to 75% of FVC (FEF(25-75%)) on 40 healthy subjects of rural/agricultural area of Sidhuwal village of Patiala City were investigated for a period from August 2008 to July 2009. Measurements were taken by spirometry according to the American Thoracic Society standards. High volume sampler (HVS) and Anderson Impactor were used to measure the concentration levels of SPM, PM(10) and PM(2.5) in ambient air of the Sidhuwal village. A significant increase in the concentration levels of SPM, PM(10) and PM(2.5) was observed due to which PFTs of the subjects showed a significant decrease in their values, more prominently in the case of children. PFTs of young subjects recovered up to some extent after the completion of burning period but the PFT values of children remained significantly lower (p<0.001) even after the completion of burning episodes. Small size particulate matter (PM(2.5) and PM(10)) affected the PFTs to a large extent in comparison to the large size particulate matter (SPM). The study indicates that ACRB is a serious environmental health hazard and children are more sensitive to air pollution, as ACRB poses some unrecoverable influence on their PFTs.

Study of issues in the development of surface EMG controlled human hand.

In the process of improvement of prosthetic devices, there have been efforts to develop satisfactorily working artificial hands but still lots of work is to be done to meet the accuracy and requirements of the human hand movement. The EMG signal has been most promising signal in development of artificial limbs. The present review paper gives the historical developments in three main sections. First part describes the EMG signal properties. Second part deals with the mathematical models developed till now for EMG signal analysis. In the third part different design approaches have been reviewed for artificial hand. First approach discussed here is on the body-powered terminal devices which are controlled by the user’s pull on the control cable to open the hand or hook and for the grip strength. Other being myoelectric controls type, an externally-powered system which uses electrical impulses, generated by contraction of the amputees own remaining muscles to operate a motor in a mechanical hand, hook or elbow. This paper presents a brief overview of above mentioned issues with regard to artificial hands.

Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals

In this investigation, extracted features ofsignals have been analyzed for the recognition of arm movements. Short-time Fourier transform and wavelet transform based on Euclidian distance were applied to reordered signals. Results show that wavelet is a more useful and powerful tool for analyzing signals, since it shows multiresolution property with a significant reduction in the computation time for eliminating resolution problems. Finally, a statistical technique of repeated factorial analysis of variance for experimental recorded data was implemented in a way to investigate the effect of class separability for multiple motions for establishing surface electromyogram–muscular force relationship.

SEMG signal analysis at acupressure points for elbow movement

Surface electromyography signals (SEMG) are the most common form of non-invasive-measurement of muscle activities. To acquire proper SEMG for particular limb function, the placement of electrodes on the skin over respective active group of muscles becomes very important. Measurement of SEMG depends on a number of factors/parameters like amplitude, time and frequency domain properties. In the present investigation, analysis was carried firstly; to study the grip force vs. SEMG parameters at acupressure points on arm, using single channel approach. At all the selected acupressure points a linear increment of SEMG was observed. Secondly; to discriminate four elbow movements from different locations on arm using two channel approach with single parameter. The parameter for the analysis chosen was the root of mean of square (RMS) value of SEMG. Further; principal component analysis was used to verify the elbow movement discrimination. Extension and supination were the two operations which were observed to be easy to realize by prosthetic devices. The selection of these locations was done on the basis of acupressure points and anatomy of elbow. Matlab-softscope was used for acquiring the SEMG from line-in input port of PC-sound card. This study will also be helpful for the researchers in understanding the behavior of SEMG for elbow movement in development of prosthetic or exoskeleton devices.

Effects of air pollution on respiratory parameters during the wheat-residue burning in Patiala.

Effects of pollution produced by wheat-residue burning on respiratory parameters of healthy inhabitants were investigated for two consecutive wheat cultivation periods (February–July in 2007 and 2008) at Patiala city of Punjab, India. A total of 51 selected subjects of the age group 13–53 were selected from five sites of Patiala for pulmonary function tests (PFTs), including force vital capacity (FVC), force expiratory volume in one second (FEV1), peak expiratory flow (PEF) and force expiratory flow between 25 to 75% of FVC (FEF25–75%) using a spirometer. High volume samplers (HVS) and an Andersen cascade impactor were also used to measure the concentration of suspended particulate matter (SPM) and particulate matter (PM) of size less than 10 μm. PFTs show significant decrease and particulate matter shows a significant increase during the burning period of wheat residue. Decrease in FVC and FEV1 did not recover even after completion of the exhaustive burning period this is a more serious concern then PEF and FEF25–75%. The results showed that the public exposure to relatively high levels of pollutants during the exhaustive burning period of wheat residue influences the PFTs of even healthy inhabitants.

Effects of exposure to rice-crop residue burning smoke on pulmonary functions and Oxygen Saturation level of human beings in Patiala (India)

Pulmonary Function Tests (PFTs) like Force Vital Capacity (FVC), Force Expiratory Volume in one second (FEV₁), Peak Expiratory Flow (PEF) and Force Expiratory Flow between 25 and 75% of FVC (FEF(25-75%)) and Oxygen Saturation (SpO₂) level of 50 healthy inhabitants with respect to rice crop residue burning were investigated for three rice cultivation periods from 2007 to 2009. The subjects were residents of five sampling sites selected in Patiala city. Concentration of Suspended Particulate Matter (SPM), Sulfur dioxide (SO₂), Nitrogen dioxide (NO₂) and Particulate Matter (PM) of size less than 10 and 2.5 μm (PM₁₀ and PM₂.₅) were measured by using High Volume Sampler (HVS) and Cascade Anderson Impactor. Results show that rice crop residue burning increases the pollution level in the ambient air, and PFTs undergo a significant decrease in their respective values. No significant change was seen in SpO₂ level during rice crop residue burning. In 2008, an increase of 10 μg m⁻³ in PM₂.₅, PM₁₀, SPM and NO₂ was associated significantly with decrease in FVC in percentages predicted -1.541, -1.002, -1.178, -0.232%, respectively. The decrease in air quality due to open rice crop residue burning has sub acute effect on pulmonary functions of healthy subjects and that SO₂ and NO₂ have less adverse effects on pulmonary functions than with different size Particulate Matter.

Wavelet denoising and evaluation of electromyogram signal using statistical algorithm

In this study, wavelet analysis has been exercised to understand the quality of surface electromyogram signal for class separability. The surface electromyogram signals were estimated with the following steps. First, the obtained signal was decomposed using wavelet transform. The decomposed coefficients were then analysed with threshold methods. With the appropriate choice of wavelet, it is possible to remove interference noise effectively in order to analyse the signal. This paper presents a comparative study of different Daubechies wavelets (db2-db14) family for analysis of arm motions. From the analysed results, it was inferred that wavelet db4 performs denoising the best among the wavelets and is suitable for accurate classification of surface electromyogram signal. Further, one-way repeated factorial Analysis of Variance (ANOVA) statistical technique was also implemented to investigate the voluntary muscular contraction relationship for different arm movements.

Processing and interpretation of surface electromyogram signal to design prosthetic device

SUMMARY The study of arm muscles for independent operations leading to prosthetic design was carried out. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for different arm motions and then repeated factorial analysis of variance (ANOVA) technique was implemented to analyze effectiveness of signal. The electronic design consisted of analog and digital signal processing and controlling circuit and mechanical assembly consisted of wrist, palm and the fingers to grip the object in addition to a screw arrangement connected to a low power DC motor and gear assembly to open or close the hand. The wrist is mechanically rotated to orient the hand in a direction suitable to pick up/hold the object. The entire set up is placed in a casing which provides a cosmetic appeal to the artificial hand and the connected arm. The design criteria include electronic control, reliability, light weight, variable grip force with ease of attachment for simple operations like opening, grasping and lifting objects of different weight with grip force slightly more than enough just like that of a natural hand.

SVM Based Gender Classification Using Iris Images

These days biometric authentication systems based on human characteristics such as face, finger, voice and iris are becoming popular among researchers. These systems identify an individual as an authentic or an imposter using a database of enrolled individuals. These systems do not provide other information about imposter such as her gender or ethnicity. Various researchers have utilized facial images for gender classification. Iris images have also been used for identification but there exists a very few references reporting the identification of human attributes such as gender with the help of iris images. In this paper gender has been identified using iris images. Statistical features and texture features using wavelets have been extracted from iris images. A classification model based on Support Vector Machine (SVM) has been developed to classify gender and an accuracy of 83.06% has been achieved in this work.