As inertial measurement units (IMUs) are used to capture gait data in real-world environments, gu... more As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.
This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditio... more This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditions using data collected with a triaxial accelerometer. Specifically, data from three axes of a triaxial accelerometer were used as the input, and various running conditions (slow, medium and fast) were considered the output of the FIS. The MATLAB ® fuzzy toolbox, which includes processes such as fuzzification, sets of fuzzy rules, fuzzy inference engine and defuzzification, was used to model the system. Mamdani-type fuzzy modelling was selected for developing the FIS. The structure of the generated fuzzy inference system includes three fuzzy rules (using if-then) and an initial set of membership functions. The performance of the proposed FIS model was assessed using the root mean square error (RMSE), mean absolute error (MAE) and non-dimensional error index (NDEI), which were found to equal 0.059, 0.213 and 0.147, respectively, for the test data. Additionally, the correlation coefficients (r) and coefficient of determination (R 2) between the FIS-predicted and the actual values were 0.89 and 0.81, respectively. Finally, the model performance accuracy was measured using Variance-Accounted-For (%VAF), which equaled 96.54%. Thus, the assessment of the overall performance suggests that the proposed FIS model has potential to detect slow, medium and fast running conditions.
Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait b... more Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in bio-mechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of-10ËšC and +6ËšC, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual's running patterns based on data obtained in real-world environments.
The objective of the present study was to investigate the time to fatigue and compare the fatigui... more 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.
Technology and health care : official journal of the European Society for Engineering and Medicine, 2014
Normally, surface electromyography electrodes are used to evaluate the activity of superficial mu... more Normally, surface electromyography electrodes are used to evaluate the activity of superficial muscles during various kinds of voluntary contractions of muscle fiber. The objective of the present study was to investigate the effect of repetitive isometric contractions on the three heads of the triceps brachii muscle during handgrip force exercise. Myoelectric signals were recorded from the lateral, long and medial heads of the triceps brachii muscle in 13 healthy males during maximal isometric contractions for 10 s of concurrent handgrip force and elbow extension. The subjects were asked to perform their contraction task five times with 3 minutes interval between two successive contractions. Decreasing electromyographic activities were found for the lateral and long heads, and increasing for the medial head throughout the 5 different contractions. Electromyographic activities were found for the lateral head with mean=199.84, SD=7.65, CV=3.83%, the long head with mean=456.76, SD=18.1...
Technology and health care : official journal of the European Society for Engineering and Medicine, 2014
The relationship between surface electromyography (EMG) and force have been the subject of ongoin... more 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. 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. 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 AN...
In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of... more 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. 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. Twenty, healthy right-handed men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 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 acceleromete...
This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomy... more 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. Twenty, right-handed healthy men (mean ± SD: age  = 26.7±3.83 y; height  = 174.47±6.3 cm; mass  = 72.79±14.36 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. 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, η2 = 0.47-0.80). 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.
Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brac... more Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brachii (BB) muscle vulnerable to overuse injury. The aim of this study was to investigate whether there are differences in the amplitude of the EMG signal of the BB muscle during fast and spin delivery, during the seven phases of both types of bowling and the kinesiological interpretation of the bowling arm for muscle contraction mechanisms during bowling. A group of 16 male amateur bowlers participated in this study, among them 8 fast bowlers (FB) and 8 spin bowlers (SB). The root mean square (EMGRMS), the average sEMG (EMGAVG), the maximum peak amplitude (EMGpeak), and the variability of the signal were calculated using the coefficient of variance (EMGCV) from the BB muscle of each bowler (FB and SB) during each bowling phase. The results demonstrate that, (i) the BB muscle is more active during FB than during SB, (ii) the point of ball release and follow-through generated higher signals than the other five movements during both bowling categories, (iii) the BB muscle variability is higher during SB compared with FB, (iv) four statistically significant differences (p < 0.05) found between the bowling phases in fast bowling and three in spin bowling, and (v) several arm mechanics occurred for muscle contraction. There are possible clinical significances from the outcomes; like, recurring dynamic contractions on BB muscle can facilitate to clarify the maximum occurrence of shoulder pain as well as biceps tendonitis those are medically observed in professional cricket bowlers, and treatment methods with specific injury prevention programmes should focus on the different bowling phases with the maximum muscle effect. Finally, these considerations will be of particular importance in assessing different physical therapy on bowler’s muscle which can improve the ball delivery performance and stability of cricket bowlers.
The aim of this paper was to verify and measure the variability in surface electromyography (sEMG... more The aim of this paper was to verify and measure the variability in surface electromyography (sEMG) of upper arm biceps brachii muscle during isometric contraction. Nine healthy right arm dominated males from three age-groups (adolescents, vicenarian, and tricenarian) were selected for the study. Electrodes were placed horizontally on three individual locations of the subjects' biceps brachii muscle, i.e., on the muscle belly of the biceps brachii, and on the upper and lower portion of the muscle belly. Average EMG (mean), standard deviation (SD), and coefficient of variations (CV, %) were calculated to verify the consistency of the different age groups' muscle activities. Moreover, EMG mean values were analyzed with two-way repeated measures analysis of variances (ANOVA) to monitor significant intra- and inter-muscle coordination. An online, three-channel connected and touch-proof wireless sensor was used to record the EMG signal. The results revealed muscle variability and consistency as well as the significant superiority on the biceps brachii muscle. This findings attempt to fulfill the gaps left by previous experiments based on EMG variability, subject's age variation, electrode placement, muscle contractions, etc. and to identify areas for further researche in the fields of rehabilitation, sports, and other biomedical concerns.
As inertial measurement units (IMUs) are used to capture gait data in real-world environments, gu... more As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.
This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditio... more This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditions using data collected with a triaxial accelerometer. Specifically, data from three axes of a triaxial accelerometer were used as the input, and various running conditions (slow, medium and fast) were considered the output of the FIS. The MATLAB ® fuzzy toolbox, which includes processes such as fuzzification, sets of fuzzy rules, fuzzy inference engine and defuzzification, was used to model the system. Mamdani-type fuzzy modelling was selected for developing the FIS. The structure of the generated fuzzy inference system includes three fuzzy rules (using if-then) and an initial set of membership functions. The performance of the proposed FIS model was assessed using the root mean square error (RMSE), mean absolute error (MAE) and non-dimensional error index (NDEI), which were found to equal 0.059, 0.213 and 0.147, respectively, for the test data. Additionally, the correlation coefficients (r) and coefficient of determination (R 2) between the FIS-predicted and the actual values were 0.89 and 0.81, respectively. Finally, the model performance accuracy was measured using Variance-Accounted-For (%VAF), which equaled 96.54%. Thus, the assessment of the overall performance suggests that the proposed FIS model has potential to detect slow, medium and fast running conditions.
Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait b... more Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in bio-mechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of-10ËšC and +6ËšC, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non-linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent discriminatory ability of the RF-based model. Additionally, the ranked order of variable importance differed across the individual runners. The results from the RF-based machine-learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual's running patterns based on data obtained in real-world environments.
The objective of the present study was to investigate the time to fatigue and compare the fatigui... more 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.
Technology and health care : official journal of the European Society for Engineering and Medicine, 2014
Normally, surface electromyography electrodes are used to evaluate the activity of superficial mu... more Normally, surface electromyography electrodes are used to evaluate the activity of superficial muscles during various kinds of voluntary contractions of muscle fiber. The objective of the present study was to investigate the effect of repetitive isometric contractions on the three heads of the triceps brachii muscle during handgrip force exercise. Myoelectric signals were recorded from the lateral, long and medial heads of the triceps brachii muscle in 13 healthy males during maximal isometric contractions for 10 s of concurrent handgrip force and elbow extension. The subjects were asked to perform their contraction task five times with 3 minutes interval between two successive contractions. Decreasing electromyographic activities were found for the lateral and long heads, and increasing for the medial head throughout the 5 different contractions. Electromyographic activities were found for the lateral head with mean=199.84, SD=7.65, CV=3.83%, the long head with mean=456.76, SD=18.1...
Technology and health care : official journal of the European Society for Engineering and Medicine, 2014
The relationship between surface electromyography (EMG) and force have been the subject of ongoin... more 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. 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. 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 AN...
In mechanomyography (MMG), crosstalk refers to the contamination of the signal from the muscle of... more 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. 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. Twenty, healthy right-handed men (mean ± SD: age = 26.7±3.83 y; height = 174.47±6.3 cm; mass = 72.79±14.36 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 acceleromete...
This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomy... more 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. Twenty, right-handed healthy men (mean ± SD: age  = 26.7±3.83 y; height  = 174.47±6.3 cm; mass  = 72.79±14.36 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. 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&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.05, η2 = 0.47-0.80). 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.
Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brac... more Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brachii (BB) muscle vulnerable to overuse injury. The aim of this study was to investigate whether there are differences in the amplitude of the EMG signal of the BB muscle during fast and spin delivery, during the seven phases of both types of bowling and the kinesiological interpretation of the bowling arm for muscle contraction mechanisms during bowling. A group of 16 male amateur bowlers participated in this study, among them 8 fast bowlers (FB) and 8 spin bowlers (SB). The root mean square (EMGRMS), the average sEMG (EMGAVG), the maximum peak amplitude (EMGpeak), and the variability of the signal were calculated using the coefficient of variance (EMGCV) from the BB muscle of each bowler (FB and SB) during each bowling phase. The results demonstrate that, (i) the BB muscle is more active during FB than during SB, (ii) the point of ball release and follow-through generated higher signals than the other five movements during both bowling categories, (iii) the BB muscle variability is higher during SB compared with FB, (iv) four statistically significant differences (p < 0.05) found between the bowling phases in fast bowling and three in spin bowling, and (v) several arm mechanics occurred for muscle contraction. There are possible clinical significances from the outcomes; like, recurring dynamic contractions on BB muscle can facilitate to clarify the maximum occurrence of shoulder pain as well as biceps tendonitis those are medically observed in professional cricket bowlers, and treatment methods with specific injury prevention programmes should focus on the different bowling phases with the maximum muscle effect. Finally, these considerations will be of particular importance in assessing different physical therapy on bowler’s muscle which can improve the ball delivery performance and stability of cricket bowlers.
The aim of this paper was to verify and measure the variability in surface electromyography (sEMG... more The aim of this paper was to verify and measure the variability in surface electromyography (sEMG) of upper arm biceps brachii muscle during isometric contraction. Nine healthy right arm dominated males from three age-groups (adolescents, vicenarian, and tricenarian) were selected for the study. Electrodes were placed horizontally on three individual locations of the subjects' biceps brachii muscle, i.e., on the muscle belly of the biceps brachii, and on the upper and lower portion of the muscle belly. Average EMG (mean), standard deviation (SD), and coefficient of variations (CV, %) were calculated to verify the consistency of the different age groups' muscle activities. Moreover, EMG mean values were analyzed with two-way repeated measures analysis of variances (ANOVA) to monitor significant intra- and inter-muscle coordination. An online, three-channel connected and touch-proof wireless sensor was used to record the EMG signal. The results revealed muscle variability and consistency as well as the significant superiority on the biceps brachii muscle. This findings attempt to fulfill the gaps left by previous experiments based on EMG variability, subject's age variation, electrode placement, muscle contractions, etc. and to identify areas for further researche in the fields of rehabilitation, sports, and other biomedical concerns.
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