Browsing by Author "Ali, Md. Asraf"

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A literature review on NoSQL database for big data processing
(International Journal of Engineering & Technology, 2018) Ahmed, Razu; Ali, Md. Asraf; Sundaraj, Kenneth
Abstract Objective: Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural issues and the real-time data mining techniques to extract the estimated valuable information. Methods: We searched the Springer Link and IEEE Xplore online databases for articles published in English language during the last seven years (between January 2011 and December 2017). We specifically searched for two keywords (“NoSQL” and “Big Data”) to find the articles. The inclusion criteria were articles on the use of performance comparison on valuable information processing in the field of Big Data through NoSQL databases. Results: In the 18 selected articles, this review identified 8 articles which provided various suitable recommendations on NoSQL databases for specific area focus on the value chain of Big Data, 5 articles described the performance comparison of different NoSQL databases, 2 articles presented the background of basics characteristics data model for NoSQL, 1 article denoted the storage in respect of cloud computing and 2 articles focused the transactions of NoSQL. Conclusion: In this literature, we presented the NoSQL databases for Big Data processing including its transactional and structural issues. Additionally, we highlight research directions and challenges in relation to Big Data processing. Therefore, we believe that the information contained in this review will incredible support and guide the progress of the Big Data processing.
Coherence in muscle activity of the biceps brachii at middle, proximal and distal tendon region among the arm wrestling contestants
(Allied Academics, 2013-02-06) Ahamed, Nizam Uddin; Sundaraj, Kenneth; Ahmad, R. Badlisha; Rahman, Matiur; Islam, Md. Anamul; Ali, Md. Asraf
The aim of this study was to analyze the electromyographic (EMG) activity of biceps brachii (BB) muscle under the same muscle contraction in three different locations. For this reason, arm wrestling contest was conducted to record the EMG signal from ten male subjects. Electrodes were placed on the three locations of upper arm BB; i.e. middle (belly) of BB (M), lower part (L) and upper part (U) of the BB belly. Average EMG (EMGAVG), root mean square (EMGRMS) and highest peak of the signal [EMGHigh(pk)] were calculated from the sum of EMG activity. The analysis of the effect of electrode placement location using ANOVA (analysis of variance) tests yielded a number of statistically significant differences. The results indicated, 1) majority of the EMG results confirmed the muscle activity was higher in the order of L, M and U, 2) among the 16 comparisons among the muscles (from winners and losers), there was main interaction found between the entire BB of winners and losers, also another 7 results displayed same interaction (p<0.05), but remaining 8 locations did not significant (p>0.05), 3) in the loser, the BB was forced to perform eccentric contraction as the forearm is being pronated and elbow was gradually being extended, on the other hand, contraction of the BB was concentric in the winner, and 4) winners (during concentric contraction) did not always produce highest EMG peak and some of the results (muscle activity) of loser’s (during eccentric contraction) revealed higher than the winners. The findings of the study contain a precious contribution to rehabilitation, biomedical and sports medicine by describing an experimental set up to measure muscle electrophysiology during physical activity in the case of arm activities.
Computational Intelligence Approaches for Prediction of Chronic Kidney Disease
(Springer, 2022-01-01) Ahmed, Md. Razu; Ali, Md. Asraf; Ahmed, Nasim; Bhuiyan, Touhid
Over the past few decades, it has been observed that there is a growing interest in the area of intelligence systems, such as Machine Learning. Machine learning has been extensively used in order to support medical specialists and clinicians in the help of forecast and diagnosis of various diseases. The aim of this study is to compare the performance of six supervision-based Machine Learning techniques, which are used in the prediction and detection of the chronic kidney disease outbreak. Machine learning techniques are used to solve clinical problems and medical diagnosis’ which have recently been developed. Hence, it is essential to have a framework that can instantly recognize the prevalence of kidney disease in thousands of samples. This research uses the chronic kidney disease dataset that contains 400 Kidney patient’s data including 25 parameters. Moreover, we evaluated the performance of six supervision-based machine learning classification techniques, which are: KNN, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes and Logistics Regression. The performance of the supervised machine learning classification techniques was validated with sensitivity, specificity, f1 measure and accuracy. In this experiment, NB and RF outperformed, they were found to be at 100% accuracy, whereas the DT achieved 98% accuracy. Moreover, the KNN, SVM, LR classification techniques achieved 96% accuracy. Our findings showed that both the Random Forest and Naïve Bayes classification techniques outperformed as compared to other classification techniques used to predict kidney disease of the patients tested. In summary, our study has emphasized the research trends and scope in relation to Chronic Kidney Disease and as well as clinical research areas by machine learning techniques, which have had an effective impact in biomedical fields.
Cross-Talk in Mechanomyographic Signals from the Forearm Muscles during Sub-Maximal to Maximal Isometric Grip Force
(PLOS ONE, 2014-05-06) Islam, Md. Anamul; Sundaraj, Kenneth; Ahmad, R. Badlishah; Sundaraj, Sebastian; Ahamed, Nizam Uddin; Ali, Md. Asraf
Purpose: This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor. Methods: Twenty, right-handed healthy men (mean 6 SD: age = 26.763.83 y; height = 174.4766.3 cm; mass = 72.79614.36 kg) performed isometric muscle actions in 20% increment from 20% to 100% of the maximum voluntary isometric contraction (MVIC). During each muscle action, MMG signals generated by each muscle were detected using three separate accelerometers. The peak cross-correlations were used to quantify the cross-talk between two muscles. Results: The magnitude of cross-talk in the MMG signals among the muscle groups ranged from, R2 x, y = 2.45–62.28%. Linear regression analysis showed that the magnitude of cross-talk increased linearly (r2 = 0.857–0.90) with the levels of grip force for all the muscle groups. The amount of cross-talk showed weak positive and negative correlations (r2 = 0.016–0.216) with the circumference and length of the forearm respectively, between the muscles at 100% MVIC. The cross-talk values significantly differed among the MMG signals due to: limb tremor (MMGTF), slow firing motor unit fibers (MMGSF) and fast firing motor unit fibers (MMGFF) between the muscles at 100% MVIC (p,0.05, g2 = 0.47–0.80). Significance: The results of this study may be used to improve our understanding of the mechanics of the forearm muscles during different levels of the grip force. Full Text Link: https://doi.org/10.1371/journal.pone.0096628
Development of fuzzy inference system for automatic tea making
(IEEE Xplore, 2017-03-27) Ahamed, Nizam Uddin; Taha, Zahari Bin; Khairuddin, Ismail B. Mohd; Rabbi, Mohammad Fazle; Sikandar, Tasriva; Palaniappan, Rajkumar; Ali, Md. Asraf; Rahman, Sam Matiur; Sundaraj, K.
In this paper, a fuzzy inference system has been developed for automatic tea making process. The system takes five inputs and gives two output which determines the grade of black tea and milk tea. Specifically, the proposed system considers five important characteristics of hot tea beverage such as water temperature, sugar, milk, brewing time and tea leaves quantity for grading the standard of the drink according to the consumer's requirement. Both black tea and milk tea can be rated with a grade based on the human expert judgment which is according to the taste and aroma of the tea. This automatic tea making system can let the users choose their preferred type of tea without figuring out the complicated process to making a cup of hot tea beverage. Full Text Link: http://doi.org/10.1109/I2CACIS.2016.7885314
Effects of anthropometric variables and electrode placement on the SEMG activity of the biceps brachii muscle during submaximal isometric contraction in arm wrestling
(De Gruyter, 2013-09-09) Ahamed, Nizam Uddin; Sundaraj, Kenneth; Ahmad, Badlishah; Rahman, Matiur; Ali, Md. Asraf; Islam, Md. Anamul
Surface electromyography (SEMG) has been widely used to analyze the biceps brachii (BB) muscle during voluntary contraction, and the effect of the interelectrode distance has been studied. However, the effect of anthropometric variations and the placement of electrodes on the BB activity during arm wrestling (i.e., during isometric contraction at a submaximal intensity) has seldom been investigated. In this study, the BB strength throughout this type of static contraction was evaluated. The SEMG signals were recorded from three locations on the BB: the muscle belly (M), near proximal (P), and distal tendon (L) regions. Twenty subjects who participated in the experiment were divided into five groups (A, B, C, D, and E). The average SEMG, root mean square, and variability of the signal were calculated using the coefficient of variance. The results indicated that the M region was more active and exhibited increased signal consistency (10.91%) compared with the other two regions (P: 24.47% and L: 19.13%). Significant differences were observed between the L and P regions and between the M and P regions (p<0.05); however, there were no differences between the M and L regions (p>0.05). The increase in the SEMG value in groups B and C was significant (p<0.05), whereas groups A, D, and E did not exhibit a significant increase (p>0.05). In addition, muscle size was the strongest predictor of strength compared with body weight and height. The results suggest that the M region displays considerable SEMG effects and signal reliability. Furthermore, the SEMG measurements were found to correlate strongly with the strength of the contractions and the muscle size, and not with weight and height. Full Text Link: https://doi.org/10.1515/bmt-2013-0005
EMG-based Classification of Forearm Muscles in Prehension Movements
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Springer, 2020-07-30) Rahman, Sam Matiur; Altwijri, Omar; Ali, Md. Asraf; Alqahtani, Mahdi
This paper aimed to classify two forearm muscles known as Flexor Carpi Ulnaris (FCU) and Extensor Carpi Radialis Longus (ECRL) using surface Electromyography (sEMG) signal during different hand prehension tasks, such as cylindrical, tip, spherical, palmar, lateral and hook while grasping any object. Thirteen Machine Learning (ML) algorithms were analyzed to compare their performance using a single EMG time domain feature called integrated EMG (IEMG). The tree-based methods have the top performance to classify the forearm muscles than other ML methods among all those 13 ML algorithms. Results showed that 4 out of 5 tree-based classifiers achieved more than 75% accuracies, where the random forest method showed maximum classification accuracy (85.07%). Additionally, these tree-based ML methods computed the variable importance in classification margin. The results showed that the lateral grasping was the most important moving variable for all those algorithms except AdaBoost where tipping was the most significant movement variable for this method. We hope, this ML- and EMG-based classification results presented in the paper may alleviate some of the problems in implementing advanced forearm prosthetics, rehabilitation devices and assistive biomedical robots.
EMG-force relationship during static contraction: Effects on sensor placement locations on biceps brachii muscle
(IOS Press, 2014-07-06) Ahamed, Nizam Uddin; Sundaraj, Kenneth; Alqahtani, Mahdi; Altwijri, Omar; Ali, Md. Asraf; Islam, Md. Anamul
BACKGROUND: The relationship between surface electromyography (EMG) and force have been the subject of ongoing investigations and remain a subject of controversy. Even under static conditions, the relationships at different sensor placement locations in the biceps brachii (BB) muscle are complex. OBJECTIVE: The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations. METHODS: Twenty-one right hand dominant male subjects (age 25.3 ± 1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40–100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation. RESULTS: The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r^2= 0.62, P < 0.05) than when placed on the lower part (r^2= 0.31, P> 0.05) and upper part of the muscle belly (r^2= 0.29, P< 0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively). CONCLUSION: These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement. Full Text Link: http://doi.org/10.3233/THC-140842
Empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder
(Scopus, 2021) Khatun, Mst. Arifa; Ali, Md. Asraf; Ahmed, Md. Razu; Noori, Sheak Rashed Haider; Sahayadhas, Arun
The objective of the research is to develop a predictive model that can significantly enhance the detection and monitoring performance of Autism Spectrum Disorder (ASD) using four supervised learning techniques. In this study, we applied four supervised-based classification techniques to the clinical ASD data obtained from 704 patients. Then, we compared the four machine learning (ML) algorithms performance across tenfold cross-validation, ROC curve, classification accuracy, F1 measure, precision, recall, and specificity. The analysis findings indicate that Support Vector Machine (SVM) achieved the uppermost performance than the other classifiers in terms of accuracy (85%), f1 measure (87%), precision (87%), and recall (88%). Our work presents a significant predictive model for ASD that can effectively help the ASD patients and medical practitioners.
Empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder
(Springer, 2020-09-30) Khatun, Mst. Arifa; Ali, Md. Asraf; Ahmed, Md. Razu; Noori, Sheak Rashed Haider; Sahayadhas, Arun
The objective of the research is to develop a predictive model that can significantly enhance the detection and monitoring performance of Autism Spectrum Disorder (ASD) using four supervised learning techniques. In this study, we applied four supervised-based classification techniques to the clinical ASD data obtained from 704 patients. Then, we compared the four machine learning (ML) algorithms performance across tenfold cross-validation, ROC curve, classification accuracy, F1 measure, precision, recall, and specificity. The analysis findings indicate that Support Vector Machine (SVM) achieved the uppermost performance than the other classifiers in terms of accuracy (85%), f1 measure (87%), precision (87%), and recall (88%). Our work presents a significant predictive model for ASD that can effectively help the ASD patients and medical practitioners.
Fuzzy logic controller design for intelligent drilling system
(IEEE Xplore, 2017-03-27) Ahamed, Nizam Uddin; Yusof, Zulkifli; Hamedon, Zamzury; Rabbi, Mohammad Fazle; Sikandar, Tasriva; Palaniappan, Rajkumar; Ali, Md. Asraf; Rahman, Sam Matiur; Sundaraj, K.
An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlike the existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation. Full Text Link: http://doi.org/10.1109/I2CACIS.2016.7885316
Longitudinal, Lateral and Transverse Axes of Forearm Muscles Influence the Crosstalk in the Mechanomyographic Signals during Isometric Wrist Postures
(PLOS ONE, 2014-08-04) Islam, Md. Anamul; Sundaraj, Kenneth; Ahmad, R. Badlishah; Sundaraj, Sebastian; Ahamed, Nizam Uddin; Ali, Md. Asraf
Problem Statement: In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of interest by the signal from another muscle or muscle group that is in close proximity. Purpose: The aim of the present study was two-fold: i) to quantify the level of crosstalk in the mechanomyographic (MMG) signals from the longitudinal (Lo), lateral (La) and transverse (Tr) axes of the extensor digitorum (ED), extensor carpi ulnaris (ECU) and flexor carpi ulnaris (FCU) muscles during isometric wrist flexion (WF) and extension (WE), radial (RD) and ulnar (UD) deviations; and ii) to analyze whether the three-directional MMG signals influence the level of crosstalk between the muscle groups during these wrist postures. Methods: Twenty, healthy right-handed men (mean 6 SD: age = 26.763.83 y; height = 174.4766.3 cm; mass = 72.79614.36 kg) participated in this study. During each wrist posture, the MMG signals propagated through the axes of the muscles were detected using three separate tri-axial accelerometers. The x-axis, y-axis, and z-axis of the sensor were placed in the Lo, La, and Tr directions with respect to muscle fibers. The peak cross-correlations were used to quantify the proportion of crosstalk between the different muscle groups. Results: The average level of crosstalk in the MMG signals generated by the muscle groups ranged from: 34.28–69.69% for the Lo axis, 27.32–52.55% for the La axis and 11.38–25.55% for the Tr axis for all participants and their wrist postures. The Tr axes between the muscle groups showed significantly smaller crosstalk values for all wrist postures [F (2, 38) = 14–63, p, 0.05, g2 = 0.416–0.769]. Significance: The results may be applied in the field of human movement research, especially for the examination of muscle mechanics during various types of the wrist postures. Full Text Link: http://doi.org/10.1371/journal.pone.0104280
Machine Learning Techniques for Predicting Surface EMG Activities on Upper Limb Muscle
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer, 2020-07-30) Roy, Joy; Ali, Md. Asraf; Ahmed, Md. Razu; Sundaraj, Kenneth
The aim of this review study is to analyze the techniques for predicting the surface EMG activities on upper limb muscles using different machine learning algorithms. In this study, we followed a systematic searching procedure to select articles from four different online databases, i.e. PubMed, Science Direct, IEEE Xplore and Biomed Central (published years between 2010 and 2018). In our searching procedure, we searched by characteristically with two keywords (“EMG” and “Machine Learning”) in the above four listed databases to find the related articles in the field of machine learning techniques for predicting surface EMG activities on upper limb muscles. From the searching of this review, we selected total 25 articles for predicting surface EMG signals on upper limb muscles, where 10 articles are provided most efficient and effective classifier of surface EMG signals, 11 articles described different hand gesture recognition using machine learning algorithms, 2 articles explained that the importance of muscles selection, 1 article presented the natural pinching technique and 1 article focus on evaluation error rate of movements. This review presents not only the machine learning techniques for prediction of surface EMG activities on upper limb muscles but also it focuses on the challenge of the machine learning techniques for predicting surface EMG data. In addition, we believe that this review also provides muscle related issues that will impact the prediction of surface EMG activities on muscle.
Mechanomyography
(Lecture Notes in Mechanical Engineering, Springer, 2020) Talib, Irsa; Sundaraj, Kenneth; Lam, Chee Kiang; Ali, Md. Asraf; Hussain, Jawad
The aim of this review article is to highlight an important application of mechanomyography as a tool to study muscle physiology related issues. Skeletal muscles are of vital significance in our body and contribute well towards all type of movements. Although, there are other techniques in vogue used for non-invasive assessment of muscle. But mechanomyography (MMG) do offer shear benefits for reliable muscle study. So, a substantial number of related articles were searched for this technical review from various databases including SCOPUS, PubMed, Science Direct, IEEE Xplore and springer link. Records were screened according to the selection criteria. The studies related to muscle physiology aspects analyzed using MMG were only selected for detailed analysis. During in depth analysis of records finally selected for this article, physiology aspects investigated via MMG were divided into seven sections including muscle stiffness, Parkinson disease, effect of dehydration, muscle contractile properties, muscle contraction mechanics, muscle temperature and muscle hypertrophy. The findings of this review suggest that MMG is a useful and reliable tool to investigate muscle physiology and it has significant applications in sports and medicine. Muscle contractile properties can be employed for future investigation on muscle fatigue, stiffness, atrophy and even functional mechanics of muscle. This review might fill the gap in knowledge in understanding of muscle physiology using MMG.
Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review
(IEEE Xplore, 2013-03-29) Islam, Md. Anamul; Sundaraj, Kenneth; Ahmad, R. Badlishah; Ahamed, Nizam Uddin; Ali, Md. Asraf
Mechanomyography (MMG) is extensively used in the research of sensor development, signal processing, characterization of muscle activity, development of prosthesis and/or switch control, diagnosis of neuromuscular disorders, and as a medical rehabilitation tool. Despite much existing MMG research, there has been no systematic review of these. This paper aims to determine the current status of MMG in sensor development, related signal processing, and applications. Six electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. From a total of 175 citations, 119 were selected for full-text evaluation and 86 potential studies were identified for further analysis. This systematic review initially reveals that the development of accelerometers for MMG is still in the initial stage. Another important finding of this paper is that sensor placement location on muscles may influence the MMG signal. In addition, we observe that the majority of research processes MMG signals using wavelet transform. Time/frequency domain analysis of MMG signals provides useful information to examine muscle. In addition, we find that MMG may be applied to diagnose muscle conditions, to control prosthesis and/or switch devices, to assess muscle activities during exercises, to study motor unit activity, and to identify the type of muscle fiber. Finally, we find that the majority of the studies use accelerometers as sensors for MMG measurements. We also observe that currently MMG-based rehabilitation is still in a nascent stage. In conclusion, we recommend further improvements of MMG in the areas of sensor development, particularly on accelerometers, and signal processing aspects, as well as increasing future applications of the technique in prosthesis and/or switch control, clinical practices, and rehabilitation. Full Text Link: http://doi.org/10.1109/JSEN.2013.2255982
Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography
(De Gruyter, 2015) Ali, Md. Asraf; Sundaraj, Kenneth; Ahmad, R. Badlishah; Ahamed, Nizam Uddin; Islam, Md. Anamul; Sundaraj, Sebastian
The objective of the present study was to investigate the time to fatigue and compare the fatiguing condition among the three heads of the triceps brachii muscle using surface electromyography during an isometric contraction of a controlled forceful hand grip task with full elbow extension. Eighteen healthy subjects concurrently performed a single 90 s isometric contraction of a controlled forceful hand grip task and full elbow extension. Surface electromyographic signals from the lateral, long and medial heads of the triceps brachii muscle were recorded during the task for each subject. The changes in muscle activity among the three heads of triceps brachii were measured by the root mean square values for every 5 s period throughout the total contraction period. The root mean square values were then analysed to determine the fatiguing condition for the heads of triceps brachii muscle. Muscle fatigue in the long, lateral, and medial heads of the triceps brachii started at 40 s, 50 s, and 65 s during the prolonged contraction, respectively. The highest fatiguing rate was observed in the long head (slope = -2.863), followed by the medial head (slope = -2.412) and the lateral head (slope = -1.877) of the triceps brachii muscle. The results of the present study concurs with previous findings that the three heads of the triceps brachii muscle do not work as a single unit, and the fiber type/composition is different among the three heads. Full Text Link: http://doi.org/10.1515/hukin-2015-0035
Recent Observations in Surface Electromyography Recording of Triceps Brachii Muscle in Patients and Athletes
(IOS Press, 2014) Ali, Md. Asraf; Sundaraj, Kenneth; Ahmad, R. Badlishah; Ahmed, Nizam Uddin; Islam, Md. Anamul
OBJECTIVE: To observe and analyse the literature on the use of surface electromyography electrodes, including the shape, size, and metal composition of the electrodes used, the interelectrode distance, and the anatomical locations on the muscle at which the electrodes are placed, for the observation of the triceps brachii muscle activity in patients and athletes. METHODS: We searched the ScienceDirect and SpringerLink online databases for articles published in the English language during the last six years (between January 2008 and December 2013). We specifically searched for the keywords “EMG” and “triceps brachii” in the full text of each of the articles. The inclusion criteria were articles on the use of surface electromyography electrodes to observe the activity of the triceps brachii muscle in patients and athletes. RESULTS: In the 23 selected articles, the activities of the triceps brachii muscle in a total of 402 subjects were measured using surface electromyography electrodes: 262 subjects in the studies that focused on the rehabilitation of patients with various disorders, and 140 subjects in the studies that focused on the sports performance of various athletes. To record the surface electromyography activity of the triceps brachii muscle, the electrodes were placed over the muscle belly or the three heads (lateral, long, and medial) of the triceps brachii muscle with diverse interelectrode distances. Seventeen studies used bipolar or triode silver/silver chloride electrodes, one study utilised bipolar gold electrodes, one study applied bipolar polycarbonate electrodes, one study used a linear array of four silver bar electrodes, one study utilised DELSYS parallel bar nickel silver electrodes, and two studies did not clearly mention the composition of the electrodes used. CONCLUSIONS: Bipolar silver/silver chloride circular-shaped electrodes are utilised more frequently than electrodes with a different metal composition and shape. The anatomical locations of the triceps brachii muscle that mainly considered for electrode placement are the lateral, long, and medial heads. A 10-mm electrode size is commonly used to measure the sEMG activity more efficiently. However, we found that an electrode size of up to 40 mm may be used to reliably measure the sEMG activity on the triceps brachii muscle. A 20-mm interelectrode distance is commonly used to measure the sEMG activity using the above mentioned muscle locations and silver/silver chloride electrodes. We also identified others factors that should be taken into account for the use of the sEMG recording technique on the triceps brachii under real-time conditions. Full Text Link: http://dx.doi.org/10.3233/ABB-140098
Rehabilitation systems for physically disabled patients: A brief review of sensor-based computerised signal-monitoring systems
(Allied Academics, 2013-03-21) Ahamed, Nizam Uddin; Sundaraj, Kenneth; Ahmad, Badlishah; Rahman, Matiur; Ali, Md. Asraf; Islam, Md. Anamul; Palaniappan, Rajkumar
This brief review addresses the existing systems and challenges and provides future recommendations on computer- and biosensor-assisted rehabilitation systems for physically disabled patients. We further list the types of sensors, technical issues, and different software and hardware technologies that are currently used in rehabilitation systems to make the whole process dynamic and real-time. The review focused on 36 consolidated studies that were found using the following keywords: rehabilitation system, sensor, and computer. The electronic databases PubMed, Scopus, and Google Scholar were searched for relevant articles that were published from 2007 through 2012. These published articles included discussion of several biosensors, automated rehabilitation systems, and the application of these systems in the affected body parts of the individuals. We found that 54 types of biosensors have been used for real-time and computer-assisted rehabilitation systems. The findings suggest that there are still some body parts (such as the muscle tendons area and abdomen) and application areas (e.g., post- and pre-pregnancy) that have not yet been targeted by biosensor-supported medical rehabilitation systems aided by suitable hardware, software, and other assistive technologies.
sEMG activities of the three heads of the triceps brachii muscle during cricket bowling
(World Scientific, 2016) Ali, Md. Asraf; Sundaraj, Kenneth; Ahmad, R. Badlishah; Ahamed, Nizam Uddin; Islam, Md. Anamul; Sundaraj, Sebastian
The aim of the present study was to analyze the surface electromyography (sEMG) activities generated by the three heads of the triceps brachii (TB) muscle among the different phases during fast and spin bowling. sEMG signals from the lateral, long and medial heads of the TB from 20 bowlers were measured individually during bowling. To analyze the sEMG activities, the root mean square (RMS) value in each bowling phase for every trial per bowler was calculated from the sEMG signals from the three heads of the TB. Higher sEMG activities at the three heads of the TB were found during the fifth phase followed by the sixth, seventh, third, fourth, second and first phases in both types of bowling. sEMG activities were significantly different among the three heads of the TB and among the seven bowling phases for both bowling types at an alpha level of p<0.05. These findings will be of particular importance for assessing different physical therapies for the three headed TB muscle which can improve the performance in ball delivery of cricket bowlers. Full Text Link: https://doi.org/10.1142/S0219519416500755
Significance of Electromyography in the Assessment of Diabetic Neuropathy
(Journal of Mechanics in Medicine and Biology, World Scientific, 2019) Rabbi, Mohammad Fazle; Ghazali, Kamarul Hawari; Altwijri, Omar; Alqahtani, Mahdi; Rahman, Sam Matiur; Ali, Md. Asraf; Sundaraj, Kenneth; Taha, Zahari; Ahamed, Nizam Uddin
Diabetic neuropathy is one of the physical complications of diabetes mellitus (DM) patients with a long history of diabetes. An electromyography (EMG)-based assessment may be very useful for the management of diabetic neuropathy. In the present study, we aimed to summarize all of the findings and recommendations obtained from previous studies that investigated the application of EMG to the assessment of diabetic neuropathy. An extensive search of the prominent electronic databases PubMed, Google Scholar and Scopus was performed to evaluate the following areas: (i) what are the muscles to be evaluated by EMG for neuropathy assessment, (ii) what type of EMG methodologies have been used and (iii) what recommendation can be made for neuropathy detection. The major findings are summarized as follows: (i) very few studies have analyzed the correlation of the EMG signals acquired from peripheral muscles affected in neuropathy with those obtained with non-neuropathic complications, such as ankle sprain; (ii) EMG has been applied for the detection of diabetic neuropathy more than diabetes treatment; and (iii) neuropathy detection using an EMG-based assessment were mainly performed for type 2 DM patients aged at least 50 years.