Добірка наукової літератури з теми "Neuromusculoskeletal modelling"

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

The central nervous system of humans and other animals modulates spinal cord activity to achieve several locomotion behaviors. Previous neuromechanical models investigated the modulation of human gait changing selected parameters belonging to CPGs (Central Pattern Generators) feedforward oscillatory structures or to feedback reflex circuits. CPG-based models could replicate slow and fast walking by changing only the oscillation’s properties. On the other hand, reflex-based models could achieve different behaviors through optimizations of large dimensional parameter spaces. However, they could not effectively identify individual key reflex parameters responsible for gait characteristics’ modulation. This study investigates which reflex parameters modulate the gait characteristics through neuromechanical simulations. A recently developed reflex-based model is used to perform optimizations with different target behaviors on speed, step length, and step duration to analyze the correlation between reflex parameters and their influence on these gait characteristics. We identified nine key parameters that may affect the target speed ranging from slow to fast walking (0.48 and 1.71 m/s) as well as a large range of step lengths (0.43 and 0.88 m) and step duration (0.51, 0.98 s). The findings show that specific reflexes during stance significantly affect step length regulation, mainly given by positive force feedback of the ankle plantarflexors’ group. On the other hand, stretch reflexes active during swing of iliopsoas and gluteus maximus regulate all the gait characteristics under analysis. Additionally, the results show that the hamstrings’ group’s stretch reflex during the landing phase is responsible for modulating the step length and step duration. Additional validation studies in simulations demonstrated that the modulation of identified reflexes is sufficient to regulate the investigated gait characteristics. Thus, this study provides an overview of possible reflexes involved in modulating speed, step length, and step duration of human gaits.

Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

Abstract Knowledge of neck muscle activation strategies prior to sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations prior to impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the Calibrated EMG-Informed Neuromusculoskeletal Modelling toolbox and three neural solutions were compared: i) static optimisation (SO), ii) EMG-assisted (EMGa) and iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p¡0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14 and 2.32 Nm) but not in lateral bending (RMSE = 1.07, 2.07 and 0.84 Nm). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental co-contractions significantly (p¡0.01) outperforming SO, which was characterised by saturation and non-physiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria prior to impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

This work presents the modelling, design, construction, and control of an exoskeleton for ankle joint flexion/extension rehabilitation. The dynamic model of the ankle flexion/extension is obtained through Euler-Lagrange formulation and is built in Simulink of MATLAB using the non-linear differential equation derived from the dynamic analysis. An angular displacement feedback PID controller, representing the human neuromusculoskeletal control, is implemented in the dynamic model to estimate the joint torque required during ankle movements. Simulations are carried out in the model for the ankle flexion/extension range of motion (ROM), and the results are used to select the most suitable actuators for the exoskeleton. The ankle rehabilitation exoskeleton is designed in SolidWorks CAD software, built through 3D printing in polylactic acid (PLA), powered by two on-board servomotors that deliver together a maximum continuous torque of 22 [kg cm], and controlled by an Arduino board that establishes Bluetooth communication with a mobile app developed in MIT App Inventor for programming the parameters of the rehabilitation therapies. The result of this work is a lightweight ankle exoskeleton, with a total mass of 0.85 [kg] including actuators (servomotors) and electronics (microcontroller and batteries), which can be used in telerehabilitation practices guaranteeing angular displacement tracking errors under 10%.

AbstractLanding manoeuvres are an integral task for humans, especially in the context of sporting activities. Such tasks often involve landing on one leg which requires the coordination of multiple muscles in order to effectively dissipate kinetic energy. However, no prior studies have provided a detailed description of the strategy used by the major lower limb muscles to perform single-leg landing. The purpose of the present study was to understand how humans coordinate their lower limb muscles during a single-leg landing task. Marker trajectories, ground reaction forces (GRFs), and surface electromyography (EMG) data were collected from healthy male participants performing a single-leg landing from a height of 0.31 m. An EMG-informed neuromusculoskeletal modelling approach was used to generate neuromechanical simulations of the single-leg landing task. The muscular strategy was determined by computing the magnitude and temporal characteristics of musculotendon forces and energetics. Muscle function was determined by computing muscle contributions to lower limb net joint moments, GRFs and lower limb joint contact forces. It was found that the vasti, soleus, gluteus maximus and gluteus medius produced the greatest muscle forces and negative (eccentric) mechanical work. Downward momentum of the centre-of-mass was resisted primarily by the soleus, vasti, gastrocnemius, rectus femoris, and gluteus maximus, whilst forward momentum was primarily resisted by the quadriceps (vasti and rectus femoris). Flexion of the lower limb joints was primarily resisted by the uni-articular gluteus maximus (hip), vasti (knee) and soleus (ankle). Overall, our findings provide a unique insight into the muscular strategy used by humans during a landing manoeuvre and have implications for the design of athletic training programs.

Cerebral palsy (CP) is a progressive neuromuscular disorder caused by a non-progressive damage to the brain during child birth or neonatal stage of life. Approximately two to four in 1000 newborns per year and over 17 million children worldwide are affected by CP, which results in motor function deficiency and subsequent gait impairment. Muscle conditions in individuals with CP is highly heterogeneous and characterised by alterations of musculoskeletal structure, motor control, as well as hypertonia, spasticity, contracture and altered muscle composition, making planning of appropriate treatments an unresolved challenge. Clinical examination, clinical history, and three-dimensional gait analysis are generally used to inform treatment planning; however, treatment outcomes to correct deformities, stabilise joints, and balance muscle power, remain poor to moderate. However, computational tools able to assess the internal biomechanics of an individual with CP might aid the treatment planning, including surgery. Neuromusculoskeletal (NMSK) models are a digital representation of the structure and physiology of the human neural, muscular, and skeletal systems. Using data obtained from gait analysis and medical imaging, NMSK models can estimate the internal loads (i.e., muscle and joint contact forces) acting on selected joints and tissues. These data may be used, first, to inform clinical decision making, and second, to predict alteration in gait post-surgery. Specifically, altered joint loading could lead to bone deformities and subsequent decline in gait quality even in asymptomatic patients, and as such early detection of altered internal biomechanics could provide a potent screening tool. Nevertheless, including NMSK model predictions to the clinical decision-making process has some challenges. Accurate estimation of muscle and joint contact forces requires collecting electromyograms (EMG) from numerous muscle sites during gait analysis. However, recording EMG data from a large set of muscles in children with CP is difficult due to excessive atrophy and small size of the lower limbs. Also, uncomfortably long data collection procedures may distress children with CP, which can prematurely end a data collection session. Fast, but effective, EMG data collection is always preferred in clinical settings, and consequently, EMGs are commonly recorded from four to five muscles in clinical gait analyses of the children with CP. This makes reliable and effective extrapolative estimation of unmeasured muscle excitations crucial to undertake NMSK modelling in clinical settings. Estimation of unmeasured muscle excitations may be facilitated using “muscle synergies”, which are the coordinated excitation of groups of muscles that are used to generate any rhythmic task (e.g., walking). Muscle synergies extracted from a group of processed EMG signals (known as measured excitations) may provide a tool to quantify motor control by finding low dimensional weighted “excitation” modules that are blended to generate the activity. Generally, there are two components of muscle synergies: synergy weights and excitation primitives, of which a linear combination produces a reconstructed set of muscle excitations. Individuals with CP exhibit muscle excitations and synergies with reduced complexity compared to their healthy counterparts, which means that synergies may be reliably extracted from the four to five EMG recordings generally collected during gait analysis of the children with CP. Given that NMSK modelling requires as input excitation data from a large set of muscles involved in movement generation, the application of muscle synergies has two-fold advantages: (i) muscle synergies could be used to estimate unmeasured muscle excitations using a small set of experimental EMG data collected in gait clinics, that (ii) in turn may potentially inform NMSK modelling to estimate internal biomechanics of muscles and joints during locomotion in children with CP. Previous studies have employed muscle synergies to inform NMSK models. However, muscle synergies have typically been identified from a small set of experimental EMG data that might not represent dynamic function of the NMSK system where a large number of muscles are involved. Usually a large set of muscle excitations (30 to 50) enabling a NMSK model, that are estimated and/or adjusted to well track experimental joint moments and EMGs when employing EMG-informed NMSK modelling, are referred to as dynamically consistent. Consequently, muscle synergies extracted from the set of dynamically consistent muscle excitations may be more neurophysiologically representative than those extracted from a small set of experimentally acquired EMG data. In this thesis the overarching goal was to develop a muscle synergy-informed NMSK modelling workflow where muscle synergies were extracted from a set of dynamically consistent muscle excitations with most appropriate synergy extraction method. Using such an approach, it was aimed to use these synergy-informed models to predict internal joint biomechanics of children with CP using minimal set of EMG recordings. Generally, the factorisation methods used to extract muscle synergies are based on strict assumptions about the data’s underlying probability distribution or within-data correlations. These assumptions, may be restrictive, and even questionable, for synergy analysis of EMGs, which a priori sets the data’s information content. That is, EMG data reflect information on the organisation of muscle excitations to generate movement, and probability distributions of EMG changes (or any data for that matter) reflecting this information content. Subsequently, factorisation methods with a priori assumption on strict distributions may generate imperfect muscle synergies unless the variation of the distribution is properly tracked. So, the neural information within varying EMG signals could also be assessed by the concomitant changes in the probability density function. As such, metrics based on the probability density function of EMG data may echo the information conveyed by the EMG signals before and after processing, and by factorisation, therefore assessing how much information was retained. Novel metrics based on the probability density function could be used to evaluate the best factorisation method that can track the change in distribution of EMG data, which would produce the best muscle synergies to represent the neural information content. Subsequently, the first study assessed four factorisation methods commonly used to extract muscle synergies from muscle excitations. Although there are many factorisation methods available for extracting muscle synergies, it is not clear why one method should be chosen over another. The selection of the factorisation methods is likely based on their availability in popular computer programs rather than theory underpinning the synergy extraction process. Assessing factorisation methods based on the EMG data’s probability distribution, a common underpinning assumption of any method, has not yet been received much attention. In this study, the four most common factorisation methods, i.e., non-negative matrix factorisation method, principal component analysis, independent component analysis, and factor analysis, were evaluated using “variance accounted for” and the similarities in the probability density function of the muscle excitations and corresponding muscle synergies. Consistent with previous work, it was found that the probability density functions of EMG data varied with locomotion speed. Furthermore, the probability distribution of EMG data is a priori assumed in most factorisation approaches, which is not compliant with this variation in probability density functions. However, the non-negative matrix factorisation was found to track the probability density function variations better than the other methods, since mathematically it did not depend on any underlying form of the EMG data’s probability distribution. As such, non-negative iv matrix factorisation method was chosen to extract muscle synergies from the muscle excitations in two further studies in this thesis. This study was published as Rabbi, M. F., Pizzolato, C., Lloyd, D. G., Carty, C. P., Devaprakash, D., & Diamond, L. E. (2020). Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running. Scientific reports, 10: 8266. The second study investigated the feasibility of estimating unmeasured muscle excitations of children with CP using EMG collected from typically developing (TD) children. It has been previously shown, and was replicated in this study, that muscle synergies identified in children with CP are a simplified version, and a subset, of those synergies (particularly excitation primitives) found in their TD counterparts. Using this fundamental finding, this study developed and evaluated a muscle synergy extrapolation method to estimate unmeasured muscle excitations in children with CP using a large set of EMG data collected from TD participants. The larger set of TD EMG linear envelopes (measured excitations) were mapped onto the CP excitation primitives using pseudoinverse least squares to produce a larger hybrid set of synergy weightings. We identified the excitation primitives extracted from the best small set of selected muscle measured excitations from children with CP. It was found that measured excitations from three to four muscles in these children were able to estimate six to seven unmeasured muscle excitations with an acceptable level of variance accounted for. Furthermore, to ensure the information content was preserved in the reconstructed muscle excitations, the variation in probability density function was also compared between the experimental and estimated muscle excitations. It was found that the probability density function in reconstructed muscle excitations were similar to that in the original muscle excitations when using muscle synergy extrapolation method. Therefore, muscle synergy extrapolation method could facilitate the estimation of unmeasured muscle excitations of children with CP in clinical settings. All findings and results of this study have been published as Rabbi, M. F., Diamond, L. E., Carty, C. P., Lloyd, D. G., Davico, G., & Pizzolato, C. (2022). A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children. Scientific reports, 12: 3599. The third study investigated if a synergy extrapolation method could be developed and incorporated with NMSK modelling to estimate the internal musculoskeletal biomechanics of children with CP with a minimal number of EMG recordings. Since a limited number of EMGs are collected in gait clinics, a comprehensive gait evaluation v using NMSK modelling might not be possible, the later which needs a large set muscle excitations as inputs. A modelling workflow is thus required to estimate the measured and unmeasured muscle excitations and internal biomechanics at the same time using only a small number of EMG recordings. Indeed, EMG-informed NMSK modelling that is limited by the number of unmeasured EMG recordings might not well estimate joint moments and contact forces. So, muscle synergies could inform such NMSK modelling to estimate unmeasured muscle excitations and subsequent internal joint biomechanics. However, the muscle synergies and excitations must be dynamically consistent, i.e., able to drive the NMSK model to well track experimental joint moments and measured EMG. To this end, since it is easier to collect EMG from a large set of muscles from TD children, synergies extracted from a healthy EMG set and an EMG-informed modelling was used to develop a synergy-informed NMSK modelling. From a minimal number of experimental EMG recordings, the synergy-informed NMSK models were able to estimate the muscle excitations and forces, and joint moments, as accurately as the current best EMG-assisted modelling, and outperformed static optimisation method. Research findings of this study will be submitted for publication as Rabbi, M. F., Davico, G., Diamond, L. E., Lloyd, D. G., Carty, C. P., & Pizzolato, C. (2022). Muscle synergy-informed neuromusculoskeletal modelling for children with cerebral palsy. Frontiers in Physiology, to be submitted. In conclusion, a muscle synergy-informed NMSK modelling workflow was developed where internal biomechanics for children with CP was estimated with a minimal set of experimental EMG data. While developing the workflow, non-negative matrix factorisation was found to be the most appropriate factorisation method to extract muscle synergies during locomotion, based similarities to the EMGs probability density function. Further, it was demonstrated how muscle synergies could be used to estimate unmeasured muscle excitations. Finally, a muscle synergy-informed NMSK model using EMG from only three or four muscles was developed to estimate muscle forces, joint moments and knee contact forces for children with CP during walking. Considering advantages in clinical data collection and in vivo estimation of internal biomechanics it appears that combining synergy-informed NMSK modelling and EMG recordings from only three muscles (e.g., soleus, semimembranosus, and vastus lateralis) could be a viable option for gait analyses in clinical settings.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Health Sci & Soc Wrk
Griffith Health
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Osteoarthritis (OA) is a common and progressive joint disease with a prevalence of 3.6-4.1% of the global population in 2010 and one of the leading causes of worldwide disability. Hip OA is less common than knee OA; although the prevalence of hip OA is 8% in people aged 85 years and over. There is no cure for hip OA, and pain and disability can be only managed through conservative therapies, or finally total hip replacement. Inappropriate hip joint loading, measured as hip joint contact forces (HJCF), during daily activities is believed to be a factor in hip OA initiation and progression. Neuromusculoskeletal (NMS) models, which are anatomical and physiological mathematical representations of an individual, can be used to estimate muscle-tendon and internal joint contact forces (JCF) during human movement. However, the musculoskeletal system is inherently indeterminant and there are infinite combinations of muscle-tendon forces that can produce the same external joint loads. Two main neural control solutions that have been employed to solve this muscle-tendon force distribution problem: optimisation and electromyography (EMG)-informed solutions. Although static optimisation is commonly used, an EMG-informed NMS approach may offer more physiologically plausible HJCF estimates, since it accounts for an individual’s unique muscle activation patterns. Indeed, individuals with hip OA have abnormal muscle activation, which may influence the HJCF. Calibration, or tuning, of the neuromuscular parameters in EMG-informed NMS modelling is required to create subject-specific models. There are various cost functions used for calibration and their selection is crucial for obtaining physiological plausible estimates of JCF. EMG-informed NMS modelling is driven by EMG signals experimentally measured with surface electrodes. However, surface electrodes cannot record deep muscles important for hip function, which limits this application of EMG-informed modelling. Recently, EMG-hybrid and EMG-assisted modes have been developed to address this limitation, although, to date, there have been only two limited studies using these EMG-informed approaches for the hip, and only in healthy individuals. Furthermore, no studies have assessed how different calibrations cost functions and EMG-informed neural control solutions affect HJCF estimates. Subsequently, this thesis aimed to assess the use of calibrated EMG-informed NMS modelling, available in the Calibrated EMG-informed Neuromusculoskeletal Modelling toolbox (CEINMS), to explore the HJCF from healthy individuals and patients with mild-to-moderate hip OA during walking. The first study compared how different calibration cost functions and EMG-informed neural solution modes affected the estimated HJCF from a healthy population. Calibrating with a cost function to minimise joint moments prediction errors and peak HJCF, used together with the EMG-assisted neural solution mode well tracked the external joint moments and measured EMGs, and was the recommended approach to calculate physiologically plausible HJCF. The study has been submitted to the Journal of Biomechanics with following author order and title: Hoang, H.X., Pizzolato, C., Diamond, L.E., Lloyd, D.G., 2017. “Subject-specific calibration of neuromuscular parameters enables neuromusculoskeletal models to estimate physiologically plausible hip joint contact forces in healthy adults.” The second study compared estimated HJCF in people with mild-to-moderate hip OA obtained with the calibrated EMG-assisted pipeline in CEINMS (recommendation from the first study) and the static optimisation pipeline in OpenSim. The results showed that EMG-assisted mode and static optimisation both well tracked lower limb joint moments. However, the EMG-assisted mode was able to track hip muscle co-contraction patterns, whereas static optimisation did not. The associated manuscript is in preparation for submission to the Journal of Biomechanics as: Hoang, H.X., Diamond, L.E., Lloyd, D.G., Pizzolato, C., 2017. “A calibrated EMG-informed neuromusculoskeletal modelling method can appropriately account for muscle co-contraction to estimate hip joint contact forces in people with hip osteoarthritis.” The third study assessed the hip muscle co-contraction and HJCF in people with mild-to-moderate hip OA and healthy individuals using the calibrated EMG-informed NMS modelling approach as recommended in the previous two studies. Higher hip muscle co-contraction and lower HJCF were observed in individuals with hip OA compared to controls. These findings challenge previous beliefs of over-loading due to muscle co-contraction in people with hip OA. The associated manuscript is in preparation for submission to Osteoarthritis and Cartilage as: Hoang H.X., Loureiro, A., Constantinou, M.,Barrett, R., Pizzolato C., Lloyd D.G., Diamond L.E., 2017. “People with symptomatic mild-to-moderate hip osteoarthritis exhibit higher muscle co-contraction and walk with lower hip joint contact forces compared to healthy people.” The findings of this thesis showed that (i) the neural solution control is essential when estimating muscle-tendon forces and HJCF with NMS models and, therefore, subject-specific muscle activation patterns should be included in any modelling framework, (ii) calibration through tracking of joint moments and peak HJCF in conjunction with the EMG-assisted neural solution mode resulted in more physiologically plausible HJCF compared to other methods, and (iii) people with mild-to-moderate hip OA walk with more hip muscle co-contraction and lower HJCF compared to healthy people.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School Allied Health Sciences
Griffith Health
Full Text

Joint instability is a recognised mechanism of abnormal loading that may play a role in the progression of osteoarthritis. Although the hip is considered a stable joint, the hip is one of three most affected joints by osteoarthritis. Recent evidence suggests individuals with hip pathology may have rotational (i.e., ‘giving way’) and/or micro (i.e., excessive femoral head translation) hip instability, which could contribute to early onset osteoarthritis. The deep hip muscles are a group of small and deeply situated muscles that are considered important for active hip stabilisation. Some rehabilitation programs target the deep hip muscles with the aim of improving joint stability. However, given their small physiological cross-sectional areas and moment arms, their force and moment generating potential is limited and may render them insufficient to contribute to hip stability or loading. Insight into the function of the deep hip muscles will establish whether targeted training of these muscles is a viable rehabilitation strategy for improving hip stability, and potentially, slowing osteoarthritis progression. The contribution of the deep hip muscles to joint stability and loading during dynamic tasks can be estimated using neuromusculoskeletal modelling approaches. Informing muscles in neuromusculoskeletal models using individual electromyography (EMG) signals (i.e., EMG-informed modelling) can provide estimates of joint loading close to measured values using instrumented implants. However, EMG signals of the deep hip muscles can only be acquired using intramuscular measurements due to the muscles’ deeply situated positions. This thesis aimed to uncover the potential function of the deep hip muscles in relation to joint stability and loading. Furthermore, we aimed to investigate whether intramuscular EMG signals of the deep hip muscles are required for investigations into hip loading and deep hip muscle function when using an EMG-informed neuromusculoskeletal modelling approach. Study 1 determined the contribution of the deep hip muscles to hip stability. Hip stability was defined as rotational hip stiffness in the sagittal plane estimated using EMG-informed modelling. A generic musculoskeletal model was adjusted to include all deep hip muscles and employed to calculate hip rotational stiffness in the sagittal plane during walking in 12 participants. Rotational hip stiffness was compared between three model configurations that differed in deep hip muscle excitations but had identical excitations for all other muscles. Results showed that the deep hip muscles contributed little to sagittal plane rotational hip stiffness during walking and had little potential to change hip rotational stiffness. These results cast doubt on the assumed function of the deep hip muscles to stabilise by stiffening the hip. Study 2 investigated the potential of the deep hip muscles to change the direction of hip loading in the acetabulum. The direction of hip contact force was calculated during walking and squatting in 12 participants using an EMG-informed neuromusculoskeletal model configured with different activation levels for the deep hip muscles. Results showed that the deep hip muscles have the potential to redirect hip contact force towards the centre of the acetabulum. Redirecting hip loading towards the centre of the acetabulum may be effective for individuals with microinstability and/or individuals who present with unfavourable regional loading and/or cartilage damage. Future investigations to hip loading should include the deep hip muscles in the neuromusculoskeletal model due to their contribution to the direction of hip loading. Study 3 determined the need for intramuscular EMG measurements of the deep hip muscles when investigating hip loading and deep hip muscle function using EMG-informed neuromusculoskeletal modelling. Hip contact forces were calculated during walking, squatting, and squat-jumping in 17 participants using an EMG-informed neuromusculoskeletal model with and without intramuscular EMG of the deep hip muscles. Hip contact force magnitude and direction were not affected by the exclusion of intramuscular EMG of the deep hip muscles. Additionally, the synthesised configuration was able to estimate high and low magnitudes of v deep hip muscle excitations, however, synthesised excitation patterns were likely too imprecise to provide valuable insight into deep hip muscle function. Taken together, the results of this thesis provide new evidence of the function of the deep hip muscles and put forward new ideas and directions for future research. Contrary to existing clinical assumptions, these muscles have limited ability to stiffen the hip but have the potential to redirect hip loading towards the centre of the acetabulum. Future studies investigating deep hip muscle function may benefit from informing the deep hip muscles with intramuscular EMG to assure accurate estimation of deep hip muscle forces. However, excitation patterns of the deep hip muscles may be synthesised in studies investigating hip loading only. Based on the results of this thesis, strengthening the deep hip muscles in rehabilitation programs may be ineffective for individuals with rotational instability, but could be beneficial for individuals who present with microinstability, unfavourable regional loading, and/or cartilage damage.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Health Sci & Soc Wrk
Griffith Health
Full Text

Background: Knee injuries have a considerable impact on both the person’s psychological and physical health. We currently have tools to address each of these aspects but they are often considered independent of each other. Little work has been done to consolidate the subjective and objective functional ability of anterior cruciate ligament (ACL) injured individuals, which can be detrimental when implementing a return-to-play decision-making scheme. The lack of understanding concerning the relationship of these two measures may account for the high incidence of re-injury rates and lower quality of life exhibited by so many of these patients. Purpose: The purpose of this doctoral thesis is to investigate the relationship between subjective and objective measures of functional ability in ACL deficient and ACL reconstructed conditions through biomechanical and neuromusculoskeletal modelling approaches. Methods: This thesis is comprised of five studies based on a single in vivo data collection protocol, medical imaging and in silico data analyses. The in vivo data collection was of test-retest design where ACL deficient patients participated prior to their operation and approximately ten months post-reconstruction. This experimental group was matched to a healthy, uninjured control group, which was tested a single time. The first study of this thesis involved a descriptive analysis of spatiotemporal, neuromuscular, and biomechanical patterns during hopping and side cut tasks in addition to subjective functional ability questionnaires. Then, two novel measures of dynamic knee joint control were developed and applied along with a third measure to determine if changes in joint control exist between the three groups (Study 2). The relationships of these objective measures of functional ability to subjective measures were then examined through correlation and regression models (Study 3). Following this, a method of including magnetic resonance imaging to construct patient-specific models was developed and implemented to determine realistic kinematic and ligament lengthening profiles (Study 4). These patient-specific models were then applied to quantify knee joint loading in the form of contact and ligament forces, which were correlated to subjective measures of functional ability (Study 5). Results: Even though no major differences in neuromuscular patterns were observed between all three groups, it was found that subjective patient-reported outcome measures scores and biomechanical measures in the form of knee flexion angles and extensor moments were lower in the ACL deficient group compared to healthy controls. These differences continued to exist 10 months post-operation as the ACL reconstructed group had not fully recovered to patterns observed in the healthy controls. The current findings also suggest a possible hierarchy in the relationships between objective and subjective measures of functional ability. Basic kinematic objective measures such as knee flexion angle show small to moderate correlations, while more comprehensive measures such as stiffness and joint compressive force show moderate to strong correlations to subjective questionnaires. Finally, this thesis developed patient-specific OpenSim models that were used to produce appropriate kinematics and ligament lengthening with the reduction in soft tissue artefact. Conclusion: This thesis demonstrated that patients who are high-functioning in the ACL deficient state show greater improvements in subjective outcome scores after ACL reconstruction compared to objective measures. Biomechanical and neuromusculoskeletal modelling approaches identified important differences between the healthy and ACL deficient groups that were not resolved post-operatively. Our results also demonstrate that certain subjective and objective measures of functional ability are strongly correlated. The knowledge gained from this test-retest design and novel patient-specific in silico models aids clinicians in managing their expectations regarding the effectiveness of reconstruction and the respective long-term sequelae.