Title: Comparative analysis of the crawl pattern and specific exercises. Purpose: The aim of the dissertation is to evaluate the coordination similarity ratio of involvement of selected muscles during the crawl swimming cycle as a target movement with imitation movement acts. Methods: The research study has the character of an intra-individual and inter- individual comparative analysis of the coordination characteristics of the movement system. This is a sequential triangulation of a quantitative-qualitative approach and an intragroup case study with an experimental way of getting data. Results: Muscle activation of selected muscles during the crawl did not show a significant difference in effect size compared to the imitation movements on the Biokinetic swimming simulator and exercising with swimming expanders. Keywords: Biokinetic, expanders, surface electromyography, swimming, swimming simulator,

Author: Bc. Denisa Hrdinová Title: The motion pattern of abduction in the shoulder joint in overhead throwing of softball Extraliga players Objectives:The aim of the thesis was to monitor and evaluate the difference in the sequence of tenses of muscle recruitment during the abduction in the shoulder joint between softball players and the general population, using surface electromyography (SEMG). A partial aim was to identify shortened muscle groups of the shoulder girdle and their influence on the timing of the abductors. Another partial aim was to evaluate the effect of shortened muscle groups on possible deficits in scapulohumeral rhythm of softball players. Methods: The experimental group (n=10) and the control group (n=10) with a total average age of 21.8 ± 1.81 (SD) participated in the kinesiological analysis with targeted examination of the shortened muscle groups and scapulohumeral rhythm. Followed by SEMG examination of muscle timing during abduction in the shoulder joint. Five muscles were monitored during 15 repetitions of motion at 70 bpm. The SEMG signal analysis was processed by rectification and smoothing in MyoResearch software by Noraxon v3.80 and statistical analysis performed in program Statistica 13.4. Results: 1. The difference in timing between the experimental and control...

Appropriate sampling strategies for estimation of exposure to physical risk factors require knowledge of exposure variability over time. Limited information is available about the variability of exposure to physical risk factors for upper extremity musculoskeletal disorders, especially during non-cyclic work activities. We investigated the magnitude and relative contributions of several sources of variance to the total exposure variance among office, custodial, or maintenance workers (N = 5 per group). In addition, we examined the homogeneity of exposure within each group of workers and exposure contrast between groups of workers. Activation of the flexor carpi radialis and upper trapezius muscle groups was assessed with surface electromyography (EMG) and wrist motion was assessed with electrogoniometry. Exposure information was collected continuously over a complete work shift on two occasions. We observed a substantial contribution of the within-day-within-subject variance component to the total exposure variance for all EMG and electrogoniometer summary measures. We also observed limited exposure contrast between the occupational groups in summary measures of upper trapezius EMG and most electrogoniometry summary measures. The large within-day-within-subject variance suggests the need for prolonged measurement durations (e.g., more than 1 hr) in future epidemiologic investigations of associations between exposure to physical risk factors and upper extremity musculoskeletal disorders.

En raison de leur implication potentielle dans la performance et la blessure, la compréhension des coordinations musculaires des ischio-jambiers représente aujourd’hui un enjeu en sciences du mouvement. Cependant, les méthodes actuelles basées sur la mesure de l’activation musculaire ou la modélisation musculo-squelettique ne permettent pas d’identifier les facteurs influençant les coordinations pour chaque individu. Explorer ces facteurs permettrait de mieux comprendre comment le système nerveux central explore et exploite l’ensemble des coordinations musculaires possibles afin d’adopter une solution permettant de réaliser la tâche demandée. Ce manuscrit a pour objectif de décrire les coordinations musculaires des ischio-jambiers à partir de données acquises in vivo. La partie expérimentale de ce travail s’articule autour de trois études qui ont exploré le couplage entre l’activation musculaire et les propriétés mécaniques des muscles. Ces travaux ont visé à comprendre les facteurs déterminants les coordinations de chaque individu, l’effet des coordinations musculaires de chaque individu sur leur performance et leur adaptation après une blessure musculaire.Les résultats de nos études montrent que les coordinations musculaires des ischio-jambiers varient substantiellement parmi les personnes actives et les athlètes de haut niveau. Nous avons montré que ces coordinations ne sont pas déterminées par les propriétés mécaniques de chaque muscle, i.e. le déséquilibre d’activation entre les ischio-jambiers n’est pas lié au déséquilibre de capacité de production de force. Ces coordinations apparaissent également plus ou moins avantageuses pour être performant dans une tâche d’endurance réalisée jusqu’à épuisement. Enfin, après la survenue d’une blessure, les coordinations musculaires diffèrent entre les jambes blessée et non blessée. Plus précisément, la contribution du muscle blessé au couple de force total est plus faible en comparaison du même muscle de la jambe opposée. Ces différences pourraient avoir des conséquences négatives à court-terme (pour la performance) et à long terme, pouvant hypothétiquement aller jusqu’à la récidive de la blessure. / The understanding of hamstrings coordination is a trending topic in human movement science because of their potential involvement in both performance and injury. However, current experimental methods based on muscle activation recording or musculoskeletal modeling do not enable the identification of the factors that influencing muscle coordination for each individual. Addressing this issue is fundamental to understand how the central nervous system explores and exploits a set of many feasible coordination to adopt a good enough solution that enables producing a given motor task. This manuscript aims at describing hamstring coordination with in vivo data. The experimental part of this work is based on three studies that explored the coupling between muscle activation and the hamstrings mechanical properties. Also, this work aims at understanding the factors that influence muscle coordination of each individual, the effect of muscle coordination on their performance and their adaptation after a muscle injury.Our results show that hamstrings coordination varies substantially among active and elite athletes. We have shown that hamstrings coordination was not determined by the mechanical properties of each muscle, i.e. the imbalance of activation between hamstrings is not related to the imbalance of force production capacity. In addition, hamstrings coordination appears more or less advantageous in order to perform during a time to exhaustion task. Finally, muscle coordination differs between injured and uninjured legs, even after the completion of rehabilitation. Specifically, the injured muscle contributes in a lower extent to hamstring total torque in comparison with the same muscle of the opposite leg. These differences could have negative consequences in the short term (for performance) and in the long term, which could hypothetically increase the risk of reinjury.

Prolonged performance of repetitive movements can lead to muscle fatigue, negatively impacting human performance. As a result, researchers have explored methods to effectively assess and quantify this phenomenon, where surface electromyography (sEMG) is a popular method to reveal information regarding muscle contractions. The continuous wavelet transform (CWT) captures the instantaneous frequency components of signals, which make it suitable for sEMG analyses of dynamic muscle contractions. Moreover, sample entropy (SampEn) can be used to quantify the complexity of the sEMG signal, which provides novel insights for assessing muscle fatigue. However, the amount of research on sEMG complexity analyses to assess muscle fatigue during dynamic contractions is limited. Therefore, the goal of this work was to: 1) calculate and compare the major frequency components (MFC) from CWT and modified SampEn (MSE) of sEMG signals during a repetitive trunk flexion-extension (F-E) task; and 2) determine which sEMG metric is more closely related to ground truth fatigue indicators including the visual analogue scale (VAS), maximum pulling force, and kinematic variability of movements. Seven male and five female participants performed up to twelve sets of 50 repetitive trunk FE movements based on pre-defined stopping criteria. Their VAS and maximum pulling strength were measured immediately after each set. The MFC from CWT and the MSE values were calculated from both the left and the right lumbar erector spinae (LES) throughout the movements. Trunk dynamic kinematic variability of every set was quantified by the spine motion composite index (SMCI). Repeated measures correlation coefficients (r) were used to calculate the relationship between MFC and MSE, as well as between these outcome variables and VAS, maximum pulling force, and SMCI across all participants. Visual inspection revealed that on average that both the MFC and the MSE of sEMG signals decreased as the fatiguing protocol progressed, where a significant correlation was found between the two sEMG metrics (r = 0.270, p = 0.006). No significant correlations were found between the two sEMG measures and the maximum pulling strength (r_MFC = 0.101, p = 0.313; r_MSE = 0.193, p = 0.051). Nevertheless, both sEMG metrics showed significant correlations with fatigue VAS, with the MFC having stronger correlations across all the participants (r_MFC = −0.602, p < 0.001) than the MSE (r_MSE = −0.248, p = 0.011). Significant negative correlations were also observed between the SMCI and both sEMG MFC (r_MFC = −0.268, p = 0.010) and MSE (r_MSE = −0.335, p = 0.001). Both sEMG metrics mapped onto the perceived fatigue and movement pattern variations during the task, suggesting they could be used for assessing fatigue during dynamic movements. However, the MFC had a stronger correlation with participants' perceived fatigue whereas MSE was more strongly correlated with kinematic variability. Continued research is required to further examine these relationships, as well as determine the best method of assessing changes in force output with muscle fatigue.

The high prevalence of work-related musculoskeletal disorders in occupational settings necessitates the development of economic, accurate, and convenient methods for quantifying biomechanical risk exposures. In terms of lifting, the occupational work environment does not provide resources for recording the start and end times of lifting tasks performed by individual workers. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts. / Master of Science / Naturalistic occupational settings involve prolonged, frequent, and physically heavy lifting-lowering tasks that are associated with a high prevalence of musculoskeletal disorders. This necessitates the development of economic, accurate, and convenient methods for quantifying risk exposures such as load magnitude, repetitiveness and duration. In terms of lifting, the occupational work environment does not provide resources for recording the start and end of lifting tasks performed by individual workers for analysis. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. Electromyographic (EMG) signals representing the neuromuscular activity associated with muscular contractions can be valuable for exposure assessment. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts.

Tracking human hand motions has raised more attention due to the recent advancements of virtual reality (Rheingold, 1991) and prosthesis control (Antfolk et al., 2010). Surface electromyography (sEMG) has been the predominant method for sensing electrical activity in biomechanical studies, and has also been applied to motion tracking in recent years. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy is proposed to predict finger joint angles and wrist angles. This strategy combines time series data of sEMG signals and simulated muscle features, which can be extracted from a biomechanical model available in OpenSim (Delp et al., 2007). A support vector regression (SVR) model is used to firstly predict muscle features from sEMG signals and then to predict joint angles from the estimated muscle features. A set of motion data containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment. A hypothesis was tested to validate whether adding the simulated muscle features would significantly improve the prediction performance. The study indicates that the biomechanical knowledge-driven data fusion strategy will improve the prediction of new types of human hand motions. The results indicate that the proposed strategy significantly outperforms the benchmark date-driven model especially when the users were performing unknown types of motions from the model training stage. The proposed model provides a possible approach to integrate the simulation models and data fusion models in human factors and ergonomics. / Master of Science

Submitted in partial compliance with the requirements for the Masters’ Degree in Technology: Chiropractic, Department of Chiropractic, Durban University of Technology, Durban, South Africa, 2014. / Background: Spinal manipulation has been shown to result in neurophysiological changes, most often noted in the paraspinal muscles. These effects have been associated with an increase in paraspinal muscle contractibility; it is unclear if this leads to an increase in paraspinal muscle endurance. Objectives: To determine the effect of sacroiliac joint (SIJ) manipulation compared to a placebo treatment of the SIJ on lumbar extensor muscle endurance time. Method: A randomised, placebo-controlled pre-test post-test experimental trial, involving 40 asymptomatic male participants divided into an intervention group receiving SIJ manipulation using an impulse adjusting instrument and a placebo group receiving a pre-load force without the delivery of an impulse thrust. Outcome measures were lumbar extensor muscle endurance time, surface electromyographic (SEMG) readings, lumbar spinal range of motion, paraspinal muscle length assessment and a subjective pain measurement. Results: There was a significant difference between the groups (p=0.004) with the SIJ manipulation group showing an increase in endurance time compared to the placebo group which showed a decrease. SEMG readings increased for both groups with no statistically significant difference between the groups (p>0.05). Only extension lumbar spinal range of motion significantly improved in both groups (p=˂0.001) with no significant differences between groups (p=0.876). Only one participant reported pain during the research procedure. Conclusions: SIJ manipulation may enhance the endurance of the paraspinal muscles. This study should be conducted in a larger sample to validate the findings.

Submitted in partial compliance with the requirements for the Masters’ Degree in Technology: Chiropractic, Department of Chiropractic, Durban University of Technology, Durban, South Africa, 2014. / Background Spinal manipulative therapy (SMT) is a commonly used therapeutic modality. It has been shown that neuromuscular reflexes are elicited during spinal manipulation resulting in changes in the surrounding muscle tonicity and seen as changes in surface electromyography. Despite this little is known about the effect that SMT may have on muscle function. Increased maximum voluntary contraction (MVC) of the paraspinal muscles has been observed following lumbar SMT compared to a control and sham treatment; however its effect on muscle endurance has not been investigated. The aim of this study was to determine the effect of lumbar SMT compared to a placebo treatment on lumbar extensor muscle endurance in asymptomatic individuals. Method This study was a quantitative double blinded, pre-test and post-test placebo controlled experimental trial. Forty asymptomatic participants were randomly allocated to one of two treatment groups. One group received a single SMT applied to the L3 vertebrae and the other received the pre-load force of the SMT but no thrust. Subjective (a self-report of pain/discomfort while performing the Biering-Sorensen test) and objective [surface electromyography (sEMG), paraspinal muscle endurance time and lumbar spine range of motion] measurements were taken pre- and post-intervention. The latest version of SPSS version (IBM SPSS Inc.) was used to analyse the data. A p-value < 0.05 was considered statistically significant. Independent t-tests were used to compare means and two-way factor ANOVA (for repeated measures) was used to compare the change in the two time points between the two treatment groups (intervention and control). RESULTS There were no statistically significant differences between the intervention and placebo groups in terms of subjective reports of pain/discomfort and objective evidence of surface EMG readings, paraspinal muscle endurance time and lumbar spine range of motion.

There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.