Muscle Fatigue in the Three Heads of the Triceps Brachii During a Controlled Forceful Hand Grip Task with Full Elbow Extension Using Surface Electromyography

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

Age-Related EMG Responses Of The Biceps Brachii Muscle Of Young Adults

Although the effect of an Electromyographic (EMG) signal on the Biceps Brachii (BB) muscle is at the forefront of human movement analysis,there is limited information regarding the importance of the differences in the age-related EMG responses during contraction.The present study aimed to compare the BB muscle activity of three different groups of young adults divided based on age and to find a relationship between surface EMG and endurance time during isometric contraction.The EMG signal was recorded in 30 healthy right-arm-dominant young male subjects during a handgrip force task.The subjects were rationally divided into one of the three age groups (ten in each group):adolescents (‘A’;aged 17.3 ± 1.4 years), vicenarians (‘V’; 24.6 ± 2.1 years),and tricenarians (‘T’; 33.2 ± 1.1 years).The muscle activation during contraction was determined as the root mean square (RMS) EMG signal normalised to the peak RMS EMG signal during a 10-s isometric contraction.The statistical analysis included linear regression to examine the relationship between the EMG amplitude and the endurance time based on five levels of contraction [60%,70%,80%,90% and 100% of the maximal voluntary contraction (MVC)],repeated measures ANOVA to assess differences among the different age groups and the coefficient of variation (CoV) to investigate the steadiness of the EMG activation. The result shows that the early age groups exhibit higher and steadier muscle activity (V: 3.65 ± 0.42 mV,11.46% and A: 3.12 ± 0.29 mV,9.29%) compared with the elderly subjects (T: 2.78 ± 0.33 mV, 11.98%).The most important finding is that the linear slope coefficient for the EMG (amplitude) as a function of time for the muscle of the ‘V’ group (r2=0.591,P0.05) and ‘A’ groups (r2=0.203, P > 0.05).The results obtained in this study can be used to improve the current understanding of the mechanics and muscle functions of the BB muscle of individuals from different age groups during isometric contraction

Use of Wireless Sensor and Microcontroller to Develop Water-level Monitoring System

This paper presents the design and development process of Wireless Data Acquisition System (WiDAS) which is a multi-sensor system for water level monitoring. It also consists of a microcontroller (ATMega8L), a data display device and an ultrasonic distance sensor (Parallax Ping). This wireless based acquisition system can communicate through RF module (Tx-Rx) from the measurement sources, such as sensors and devices as digital or analog values over a period of time. The developed system has the option to store the data in the computer memory. It was tested in real time and showed continuous and correct data. The developed system is consisting of a number of features, such as low energy consumption, easy to operate and well-built invulnerability, which cangive successful results to measure the water level. Finally, its flexibility facilitates an extensive application span for self-directed data collection with trustworthy transmission in few sparse points over huge areas

Fuzzy Logic Controller Design for Intelligent Drilling System

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. Unlikethe 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

Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy

The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility

Walking speed classification from marker-free video images in two-dimension using optimum data and a deep learning method

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method

Age Related Differences in the Surface EMG Signals on Adolescent's Muscle during Contraction

EMG signal among five different age groups of adolescent's muscle. Fifteen healthy adolescents participated in this study and they were divided into five age groups (13, 14, 15, 16 and 17 years). Subjects were performed dynamic contraction during lifting a standard weight (3-kg dumbbell) and EMG signals were recorded from their Biceps Brachii (BB) muscle. Two common EMG analysis techniques namely root mean square (RMS) and mean absolute values (MAV) were used to find the differences. The statistical analysis was included: linear regression to examine the relationships between EMG amplitude and age, repeated measures ANOVA to assess differences among the variables, and finally Coefficient of Variation (CoV) for signal steadiness among the groups of subjects during contraction. The result from RMS and MAV analysis shows that the 17-years age groups exhibited higher activity (0.28 and 0.19 mV respectively) compare to other groups (13-Years: 0.26 and 0.17 mV, 14-years: 0.25 and 0.23 mV, 15-Years: 0.23 and 0.16 mV, 16-years: 0.23 and 0.16 mV respectively). Also, this study shows modest correlation between age and signal activities among all age group's muscle. The experiential results can play a pivotal role for developing EMG prosthetic hand controller, neuromuscular system, EMG based rehabilitation aid and movement biomechanics, which may help to separate age groups among the adolescents

Age-related EMG responses of the biceps brachii muscle of young adults.

Although the effect of an Electromyographic (EMG) signal on the Biceps Brachii (BB) muscle is at the forefront of human movement analysis, there is limited information regarding the importance of the differences in the age-related EMG responses during contraction. The present study aimed to compare the BB muscle activity of three different groups of young adults divided based on age and to find a relationship between surface EMG and endurance time during isometric contraction. The EMG signal was recorded in 30 healthy right-arm-dominant young male subjects during a handgrip force task. The subjects were rationally divided into one of the three age groups (ten in each group): adolescents (‘A’; aged 17.3 ± 1.4 years), vicenarians (‘V’; 24.6 ± 2.1 years), and tricenarians (‘T’; 33.2 ± 1.1 years). The muscle activation during contraction was determined as the root mean square (RMS) EMG signal normalised to the peak RMS EMG signal during a 10-s isometric contraction. The statistical analysis included linear regression to examine the relationship between the EMG amplitude and the endurance time based on five levels of contraction [60%, 70%, 80%, 90% and 100% of the maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the different age groups and the coefficient of variation (CoV) to investigate the steadiness of the EMG activation. The result shows that the early age groups exhibit higher and steadier muscle activity (V: 3.65 ± 0.42 mV, 11.46% and A: 3.12 ± 0.29 mV, 9.29%) compared with the elderly subjects (T: 2.78 ± 0.33 mV, 11.98%). The most important finding is that the linear slope coefficient for the EMG (amplitude) as a function of time for the muscle of the ‘V’ group (r2=0.591, P0.05) and ‘A’ groups (r2=0.203, P > 0.05). The results obtained in this study can be used to improve the current understanding of the mechanics and muscle functions of the BB muscle of individuals from different age groups during isometric contraction

Time and Frequency Domain Features of EMG Signal during Islamic Prayer (Salat)

Electromyography (EMG) activity of muscles can help us to assess the muscular functions during daily activity. In this paper, we investigate the EMG based assessment of the muscles situated in dorsal side of human body. Specifically, two upper and lower back muscles named as erector spinae and trapezius muscles are investigated during the body movements involved in Islamic prayer (Salat). Several time and frequency domain features of the EMG signal were examined to find the significant variation in the muscles activity. Results show that, both muscles maintain a balance in terms of contraction and relaxation during bowing and prostration position of Salat. In addition, the frequency domain features indicate that, the lumbar spine muscle exhibits contraction in each alternate position during the prayer. The finding of the study may help to develop rehabilitation program for the senior citizens suffering from back pain that restrain them to perform obligatory Salat

The Effects of Rest Interval on Electromyographic Signal on Upper Limb Muscle during Contraction

In this paper, the Electromyographic (EMG) signal was investigated on the Biceps Brachii muscle during dynamic contraction with two different rest intervals between trials. The EMG signal was recorded from 10 healthy right-arm-dominant young subjects during load lifting task with a standard 3-kg dumbbell for 10 seconds. Root mean square (RMS) has been used to identify the muscle function. The resting period was 2- and 5-minutes between each trial. The statistical analysis techniques included in the study were i) linear regression to examine the relationship between the EMG amplitude and the endurance time, ii) repeated measures ANOVA to assess differences among the different trials and iii) the coefficient of variation (CoV) to investigate the steadiness of the EMG activation. Results show that EMG signal is more active after 5 minutes rest period compare to 2-minutes gap. On the other hand, EMG signals were steady during 2-minutes rest (7.59%) compare to 5-minutes resting interval (16.14%). Results suggest that moderate interval between each trial is better to identify the muscle activity compare to a very short interval. The findings of this study can be used to improve the current understanding of the mechanics and muscle functions of the upper limb muscle of individuals during contraction which may prevent from muscle fatigue