Mechanical factors affecting the estimation of tibialis anterior force using an EMG-driven modelling approach

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe tibialis anterior (TA) muscle plays a vital role in human movement such as walking and running. Overuse of TA during these movements leads to an increased susceptibility of injuries e.g. chronic exertional compartment syndrome. TA activation has been shown to be affected by increases in exercise, age, and the external environment (i.e. incline and footwear). Because activation parameters of TA change with condition, it leads to the interpretation that force changes occur too. However,activation is only an approximate indicator of force output of a muscle. Therefore, the overall aim of this thesis was to investigate the parameters affecting accurate measure of TA force, leading to development of a subject-specific EMG-driven model, which takes into consideration specific methodological issues. The first study investigated the reasons why the tendon excursion and geometric method differ so vastly in terms of estimation of TA moment arm. Tendon length changes during the tendon excursion method, and location of the TA line of action and irregularities between talus and foot rotations during the geometric method, were found to affect the accuracy of TA moment arm measurement. A novel, more valid, method was proposed. The second study investigated the errors associated with methods used to account for plantar flexor antagonist co-contraction. A new approach was presented and shown to be, at worse, equivalent to current methods, but allows for accounting throughout the complete range of motion. The final study utilised the outputs from studies one and two to directly measure TA force in vivo. This was used to develop, and validate, an EMG-driven TA force model. Less error was found in the accuracy of estimating TA force when the contractile component length changes were modelled using the ankle, as opposed to the muscle. Overall, these findings increase our understanding of not only the mechanics associated with TA and the ankle, but also improves our ability to accurately monitor these.Headley Court Trust and the Defence Medical Rehabilitation Centre

Mechanical factors affecting the estimation of tibialis anterior force using an EMG-driven modelling approach

The tibialis anterior (TA) muscle plays a vital role in human movement such as walking and running. Overuse of TA during these movements leads to an increased susceptibility of injuries e.g. chronic exertional compartment syndrome. TA activation has been shown to be affected by increases in exercise, age, and the external environment (i.e. incline and footwear). Because activation parameters of TA change with condition, it leads to the interpretation that force changes occur too. However,activation is only an approximate indicator of force output of a muscle. Therefore, the overall aim of this thesis was to investigate the parameters affecting accurate measure of TA force, leading to development of a subject-specific EMG-driven model, which takes into consideration specific methodological issues. The first study investigated the reasons why the tendon excursion and geometric method differ so vastly in terms of estimation of TA moment arm. Tendon length changes during the tendon excursion method, and location of the TA line of action and irregularities between talus and foot rotations during the geometric method, were found to affect the accuracy of TA moment arm measurement. A novel, more valid, method was proposed. The second study investigated the errors associated with methods used to account for plantar flexor antagonist co-contraction. A new approach was presented and shown to be, at worse, equivalent to current methods, but allows for accounting throughout the complete range of motion. The final study utilised the outputs from studies one and two to directly measure TA force in vivo. This was used to develop, and validate, an EMG-driven TA force model. Less error was found in the accuracy of estimating TA force when the contractile component length changes were modelled using the ankle, as opposed to the muscle. Overall, these findings increase our understanding of not only the mechanics associated with TA and the ankle, but also improves our ability to accurately monitor these

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 +/- 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 +/- 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN +/- SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 +/- 4.0 [22.3, 40.8] and 11.0 +/- 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value &lt; 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs

Relation of muscular contractions to mechanical deformation in the human tibia during different locomotive activities

As one of the major hard tissue in humans and most vertebrates, the skeleton, generally referring to bone, provides the essential frame to support the body and to thus permit locomotion. Considering the functional requirements of bones across different species, e.g. from rats to dinosaurs, or during different growth periods, e.g. from embryo to old age, it is not difficult to conceive that bones adapt to the experienced mechanical environment. In fact, mechanically regulated bone modeling and remodeling is one of the major means to maintain regular bone metabolism. The findings on the bone adaptation to the mechanical environment have been well theorized by Julius Wolff in 1890s [1] as ‘Wolff’s law’ and refined later by Harold Frost as ‘mechanostat’ [2-4]. Evidence from numerous animal studies in the past revealed the adaptation process of the bones to the well-defined artificial mechanical environment and suggested certain relationship between the adaptation in relation to the types of loading, e.g. loading amplitude, loading cycle, loading frequency and so on [5-8]. Conversely, bone degradation was generally observed during disuse, e.g. prolonged bed rest [9], or in the microgravity environment during space flight [10]. Indeed, the best way to further our understanding in this adaptation process is to quantitatively study the mechanical loading on bone during daily locomotor activities. However, this is still rather challenging due to technical difficulties. More importantly, the mechanical load on bones can vary greatly across individuals or species, as the variance between the body size, locomotor pattern and speed

Neuromechanics of explosive performance for movement control and joint stabilisation

The broad aim of this thesis was to progress understanding of the neuromechanics of joint stability and injury mechanisms by investigating the interactions between neuromuscular function and balance perturbations as well as the influence of sex and fatigue on these variables. Knee extensor (KE) and plantar flexor (PF) isometric strength parameters (maximum voluntary torque (MVT), explosive voluntary torque (EVT)) were related in young healthy adults. EVT of KE and PF were correlated at 4/5 time points during the rising torque-time curve for all absolute (r = 0.488-0.755) and relative (to body mass (BM) (r = 0.517-0.669) and MVT (r = 0.353-0.480)) expressions of EVT. These results suggest that KE and PF function is related for both maximum and explosive torque. Males were stronger for KE (+89%) and PF (+55%) than females. Males also displayed greater EVT at all time points in KE (+57-109%) and at 50-150 ms in PF (+33-52%). When MVT and EVT were normalised to BM, males continued to be stronger at all time points in KE (+23-60%) and from 100-150 ms (18-20%) in PF. No sex differences were found when EVT was normalised to MVT. Furthermore, sex differences were discovered in muscle morphology. Females had a smaller knee flexor (KF):KE size ratio, a proportionately small sartorius (SA) and gracilis (GR) and a proportionately larger vastus lateralis (VL), potentially predisposing females to greater risk of ACL injury. Females had a larger biceps femoris long head (BFlh) as a proportion of the KF than males, which may contribute to the higher risk of hamstring strain injury (HSI) in males. Regarding explosive performance and perturbation response, explosive PF torque had a weak to moderate correlation with COM displacement (COMD) from 400-500 ms (r = -0.346 to -0.508) and COM velocity (COMV) from 300-500 ms (r = -0.349 to -0.416), with weaker correlations between explosive KE torque and COMV at 400 ms (r = -0.381 to -0.411) but not with COMD. These findings suggest that greater explosive torque results in better control of the COM in response to unexpected perturbations. The effects of football simulated fatigue on these factors resulted in reduced maximal KF and KE torque. However, football simulated fatigue was not found to reduce EVT of either muscle group, or explosive H/Q ratio. Football simulated fatigue resulted in impaired balance response to unexpected perturbation in the posterior but not the anterior direction

An EMG-Driven Cervical Spine Model for the Investigation of Joint Kinetics: With Application to a Helicopter Pilot Population

As the workforce has been shifting from manufacturing to office work, reports of neck pain have been on the rise. Unfortunately, the mechanism for the development of chronic neck pain still remains disputed. Most current cervical spine biomechanical models are aimed at the simulation of whiplash and are forward models employing the finite element method or multibody dynamics that are ill-equipped for incorporating motion capture data, with even fewer models capable of interfacing with electromyography (EMG) data. Therefore, there is a considerable opportunity to develop an inverse dynamic model that can drive muscle forces using EMG with the goal of determining the joint mechanics that could lend insight to the loading patterns and injury mechanics in the cervical spine. The current model is an inverse dynamic multi-body model of the whole cervical spine, head, and thorax. It was created entirely in Python, using anatomical data obtained from the Anatomography project, which were rescaled to match dimensions from a 50th percentile male. Constitutive expressions for ligaments are described by nonlinear springs, while the disc and facet joints are lumped into exponential rotational springs. Active muscle forces are estimated from EMG using a Hill-type muscle modeling framework. The model has endured a rigorous validation procedure comparing its predicted compression and shear values to a previously published model. The gains for each muscle were analyzed to evaluate how well muscle forces are being predicted from EMG. Finally, a sensitivity analysis was conducted to identify if the outputs of the model were overly dependent on the numeric value of a specific parameter. Overall, compression and mediolateral shear values were in good agreement with the previous model, while anteroposterior shear values were significantly smaller in magnitude. Despite this, muscle gains were, in some cases, alarmingly high. Finally, the sensitivity analysis revealed that the model is somewhat sensitive to ligament and muscle slack lengths, albeit to a much lesser extent than previously published models. The model was used to evaluate the change in joint kinetics with a flexed posture compared to a neutral one. With 45 degrees of flexion, compressive forces increased twofold throughout the cervical spine. In addition, anteroposterior shear tended to increase fourfold in the upper cervical spine, however, equalized with a neutral posture around the C4-C5 level. These findings may have implications for injury mechanisms, as a flexed posture under compression has been strongly associated with the development of posterior disc prolapse. In addition, the model was used to assess the joint kinetics from an existing data set on helicopter pilots who are required to wear night vision goggles during night flights. The classic solution to the anteriorly placed weight of the night vision goggles has been to counterbalance it with a posterior counterweight. While this works theoretically in a neutral posture, once a deviated posture is assumed, joint kinetics correspondingly increase. Adding a helmet increased the compression at C5-C6 from 204 N to 258 N, a 26% increase. Furthermore, adding night vision goggles and a counterweight increased it by 60%. Increasing the mass of the head-segment leads to an increase of compression

The re-education of upper limb movement post stroke using iterative learning control mediated by electrical stimulation

An inability to perform tasks involving reaching is a common problem following stroke. Evidence supports the use of robotic therapy and electrical stimulation (ES) to reduce upper limb impairments following stroke, but current systems may not encourage maximal voluntary contribution from the participant. This study developed and tested iterative learning control (ILC) algorithms mediated by ES, using a purpose designed robotic workstation, for upper limb rehabilitation post stroke. Surface electromyography (EMG) which may be related to impaired performance and function was used to investigate seven shoulder and elbow muscle activation patterns in eight neurologically intact and five chronic stroke participants during nine tracking tasks. The participants’ forearm was supported using a hinged arm-holder, which constrained their hand to move in a two dimensional horizontal plane.Outcome measures taken prior to and after an intervention consisted of the Fugl-Meyer Assessment (FMA) and the Action Research Arm Test (ARAT), isometric force and error tracking. The intervention for stroke participants consisted of eighteen sessions in which a similar range of tracking tasks were performed with the addition of responsive electrical stimulation to their triceps muscle. A question set was developed to understand participants’ perceptions of the ILC system. Statistically significant improvements were measured (p?0.05) in: FMA motor score, unassisted tracking, and in isometric force. Statistically significant differences in muscle activation patterns were observed between stroke and neurologically intact participants for timing, amplitude and coactivation patterns. After the intervention significant changes were observed in many of these towards neurologically intact ranges. The robot–assisted therapy was well accepted and tolerated by the stroke participants. This study has demonstrated the feasibility of using ILC mediated by ES for upper limb stroke rehabilitation in the treatment of stroke patients with upper limb hemiplegia

Variability in neuromotor control of the musculoskeletal system dynamics - A stochastic modelling approach

Pain, injuries or diseases might affect how we (are able to) coordinate movement. Therefore, an in-depth understanding of motor control, human movement dynamics and how pathologies affect movement coordination is essential to inform clinical practice that aims to improve the quality of movement in patients and therewith their quality of life. Musculoskeletal models allow for efficient simulations of human movement dynamics to predict the forces in muscles and joints in a non-invasive manner. However, assumptions on motor control are required to solve Bernstein’s problem of muscle redundancy: the large number of muscles relative to the number of joints requires the controller, our central nervous system, to choose how each muscle contributes to the forces that result in the intended movement. For healthy people, it seems reasonable to assume that we control our muscles following an optimality principle: to minimize the amount of metabolic energy spent on the task. However, a disease, pain or instability are likely to influence a patient’s control strategy; muscle control might be less optimal and more, or less, variable, depending on a person’s ability or need to control force production. Therefore, the general aim of this thesis was to explore the variability in motor control of the musculoskeletal dynamics during walking through a stochastic modelling approach. Firstly, I discussed the theoretical framework to model human movement dynamics and the current efforts to verify and validate musculoskeletal models, with the aim to quantify the errors in their predictions. Secondly, I aimed to explore the influence of motor control on the mechanical load experienced by the joints of the lower limb during level walking. An optimization approach to motor control showed that alternative motor control strategies have the potential to reduce the loading in the knee and the hip, but not in the ankle, during level walking. These results suggest that neuromuscular rehabilitation can be targeted as a conservative treatment when the mechanical load on joints is a determinant of the onset and/or progression of a disease. However, these alternative motor control strategies come at a cost of a moderate increase in the loading at non-targeted joints. Subsequently, the assumption of a lightly sub-optimal motor control strategy to predict knee contact forces, through a stochastic approach to model motor control, captured the measured intra-subject variability in these forces during multiple gait cycles of a patient with a knee replacement. Therefore, the assumption of sub-optimal control can predict a range of plausible joint contact forces, representative of the uncertainty in terms of measurement inaccuracies, modelling errors and inherent variability, which is likely to contain the true force. However, if a higher accuracy of predicted muscle and joint contact forces is required or in case of severely sub-optimal motor control, I believe the only solution is to include an explicit model of motor control. A refined mechanistic model would allow for the differentiation between hierarchical levels of motor control, as proposed by Bernstein, such as the involuntary spinal control and the cognition-driven anticipatory control

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research