Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces.

Gait data collection from overweight individuals walking on irregular surfaces is a challenging task that can be addressed using inertial measurement unit (IMU) sensors. However, it is unclear how many IMUs are needed, particularly when body attachment locations are not standardized. In this study, we analysed data collected from six body locations, including the torso, upper and lower limbs, to determine which locations exhibit significant variation across different real-world irregular surfaces. We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. Our results revealed two combinations of body locations, including the thigh and shank (i.e., the left and right shank, and the right thigh and right shank), from which IMU data should be collected to accurately classify walking patterns over real-world irregular surfaces (with classification accuracies of 97.24 and 95.87%, respectively). Our findings suggest that the identified numbers and locations of IMUs could potentially reduce the amount of data recorded and processed to develop a fall prevention system for overweight individuals.

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data.

Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.

Subject-specific and group-based running pattern classification using a single wearable sensor.

The objective of this study was to determine whether subject-specific or group-based models provided better classification accuracy to identify changes in biomechanical running gait patterns across different inclination conditions. The classification process was based on measurements from a single wearable sensor using a total of 41,780 strides from eleven recreational runners while running in real-world and uncontrolled environment. Biomechanical variables included pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence were recorded during running on three inclination grades: downhill, -2° to -7°; level, -0.2° to +0.2°; and uphill, +2° to +7°. An ensemble and non-linear machine learning algorithm, random forest (RF), was used to classify inclination condition and determine the importance of each of the biomechanical variables. Classification accuracy was determined for subject-specific and group-based RF models. The mean classification accuracy of all subject-specific RF models was 86.29%, while group-based classification accuracy was 76.17%. Braking was identified as the most important variable for all the runners using the group-based model and for most of the runners based on a subject-specific models. In addition, individual runners used different strategies across different inclination conditions and the ranked order of variable importance was unique for each runner. These results demonstrate that subject-specific models can better characterize changes in gait biomechanical patterns compared to a more traditional group-based approach.

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions.

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 biomechanical 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 -10° C and +6° C, 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.

Investigation of the EMG activity of erector spinae and trapezius muscles during Islamic prayer (Salat).

This study aimed to investigate the electrical activity of two muscles located at the dorsal surface during Islamic prayer (Salat). Specifically, the electromyography (EMG) activity of the erector spinae and trapezius muscles during four positions observed while performing Salat, namely standing, bowing, sitting and prostration, were investigated. Seven adult subjects with an average age of 28.1 (± 3.8) years were included in the study. EMG data were obtained from their trapezius and erector spinae muscles while the subjects maintained the specific positions of Salat. The EMG signal was analysed using time and frequency domain features. The results indicate that the trapezius muscle remains relaxed during the standing and sitting positions while the erector spinae muscle remains contracted during these two positions. Additionally, during the bowing and prostration positions of Salat, these two muscles exhibit the opposite activities: the trapezius muscle remains contracted while the erector spinae muscle remains relaxed. Overall, both muscles maintain a balance in terms of contraction and relaxation during bowing and prostration position. The irregularity of the neuro-muscular signal might cause pain and prevent Muslims from performing their obligatory prayer. This study will aid the accurate understanding of how the back muscles respond in specific postures during Salat.

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.

Investigation of the EMG-time relationship of the biceps Brachii muscle during contractions.

[Purpose] This study investigated the changes in the slope of EMG-time curves (relationship) at the maximal and different levels of dynamic (eccentric and concentric) and static (isometric) contractions. [Subjects and Methods] The subject was a 17 year-old male adolescent. The surface EMG signal of the dominant arm's biceps brachii (BB) was recorded through electrodes placed on the muscle belly. [Results] The results obtained during the contractions show that the regression slope was very close to 1.00 during concentric contraction, whereas those of eccentric and isometric contractions were lower. Significant differences were found for the EMG amplitude and time lags among the contractions. [Conclusion] The results show that the EMG signal of the BB varies among the three modes of contraction and the relationship of the EMG amplitude with a time lag gives the best fit during concentric contraction.

Analysis of crosstalk in the mechanomyographic signals generated by forearm muscles during different wrist postures.

INTRODUCTION: In this study, we analyzed the crosstalk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles of the forearm during wrist flexion (WF) and extension (WE) and radial (RD) and ulnar (UD) deviations. METHODS: Twenty right-handed men (mean ± SD age=26.7 ± 3.83 years) performed the wrist postures. During each wrist posture, MMG signals were detected using 3 accelerometers. Peak cross-correlations were used to quantify crosstalk. RESULTS: The level of crosstalk ranged from 1.69 to 64.05%. The wrist postures except the RD did not influence the crosstalk significantly between muscle pairs. However, muscles of the forearm compartments influenced the level of crosstalk for each wrist posture significantly. CONCLUSIONS: The results may be used to improve our understanding of the mechanics of the forearm muscles during wrist postures.

EMG-force relationship during static contraction: Effects on sensor placement locations on biceps brachii muscle.

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} = 0.61, 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.

Longitudinal, lateral and transverse axes of forearm muscles influence the crosstalk in the mechanomyographic signals during isometric wrist postures.

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 ± 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 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, η2 = 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.

Evaluation of repetitive isometric contractions on the heads of triceps brachii muscle during grip force exercise.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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=18.10, CV=3.96%, and the medial head with mean=505.16, SD=8.47, CV=1.68%. Electromyographic activities among the three heads of triceps brachii were significantly different (F=3.82) at the alpha level of (p<0.05). CONCLUSIONS: These findings support that repetitive isometric contractions decrease the contractile activity in the lateral and long heads, and increases in the medial head of the triceps brachii muscle during handgrip force exercise with full elbow extension, and the electromyographic activity changes are observed to be more significant at the long head as compared to the lateral and medial heads.

EMG-force relationship during static contraction: effects on sensor placement locations on biceps brachii muscle.

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 (r2=0.62, P<0.05) than when placed on the lower part (r2=0.31, P>0.05) and upper part of the muscle belly (r2=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.

Cross-talk in mechanomyographic signals from the forearm muscles during sub-maximal to maximal isometric grip force.

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 ± 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. 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, η2 = 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.

Hybrid markerless tracking of complex articulated motion in golf swings.

Sports video tracking is a research topic that has attained increasing attention due to its high commercial potential. A number of sports, including tennis, soccer, gymnastics, running, golf, badminton and cricket have been utilised to display the novel ideas in sports motion tracking. The main challenge associated with this research concerns the extraction of a highly complex articulated motion from a video scene. Our research focuses on the development of a markerless human motion tracking system that tracks the major body parts of an athlete straight from a sports broadcast video. We proposed a hybrid tracking method, which consists of a combination of three algorithms (pyramidal Lucas-Kanade optical flow (LK), normalised correlation-based template matching and background subtraction), to track the golfer's head, body, hands, shoulders, knees and feet during a full swing. We then match, track and map the results onto a 2D articulated human stick model to represent the pose of the golfer over time. Our work was tested using two video broadcasts of a golfer, and we obtained satisfactory results. The current outcomes of this research can play an important role in enhancing the performance of a golfer, provide vital information to sports medicine practitioners by providing technically sound guidance on movements and should assist to diminish the risk of golfing injuries.

Surface electromyographic analysis of the biceps brachii muscle of cricket bowlers during bowling.

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.

Effects of anthropometric variables and electrode placement on the SEMG activity of the biceps brachii muscle during submaximal isometric contraction in arm wrestling.

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.

Mechanomyogram for muscle function assessment: a review.

BACKGROUND: Mechanomyography (MMG) has been extensively applied in clinical and experimental practice to examine muscle characteristics including muscle function (MF), prosthesis and/or switch control, signal processing, physiological exercise, and medical rehabilitation. Despite several existing MMG studies of MF, there has not yet been a review of these. This study aimed to determine the current status on the use of MMG in measuring the conditions of MFs. METHODOLOGY/PRINCIPAL FINDINGS: Five electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. Two authors independently assessed selected articles using an MS-Word based form created for this review. Several domains (name of muscle, study type, sensor type, subject's types, muscle contraction, measured parameters, frequency range, hardware and software, signal processing and statistical analysis, results, applications, authors' conclusions and recommendations for future work) were extracted for further analysis. From a total of 2184 citations 119 were selected for full-text evaluation and 36 studies of MFs were identified. The systematic results find sufficient evidence that MMG may be used for assessing muscle fatigue, strength, and balance. This review also provides reason to believe that MMG may be used to examine muscle actions during movements and for monitoring muscle activities under various types of exercise paradigms. CONCLUSIONS/SIGNIFICANCE: Overall judging from the increasing number of articles in recent years, this review reports sufficient evidence that MMG is increasingly being used in different aspects of MF. Thus, MMG may be applied as a useful tool to examine diverse conditions of muscle activity. However, the existing studies which examined MMG for MFs were confined to a small sample size of healthy population. Therefore, future work is needed to investigate MMG, in examining MFs between a sufficient number of healthy subjects and neuromuscular patients.