ORCID Profile
0000-0002-6176-3277
Current Organisations
Griffith University Griffith Health
,
Queensland University of Technology
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Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1016/J.CLINBIOMECH.2019.03.014
Abstract: Selective dorsal rhizotomy aims to reduce spasticity in children with cerebral palsy. Early investigations indicated postoperative weakness, whereas more recent studies showed that selective dorsal rhizotomy either does not change or improves muscle strength. All previous studies assessed muscle strength in a static position, which did not represent the walking situation. The aim of this study was to analyze the influence of selective dorsal rhizotomy on muscle forces during gait. Motion capture data of 25 children with spastic cerebral palsy and 10 typically developing participants were collected. A musculoskeletal OpenSim model was used to calculate joint kinematics, joint kinetics and muscle forces during gait. Static optimization and an electromyography-informed approach to calculate muscle forces were compared. A Muscle-Force-Profile was introduced and used to compare the muscle forces during walking before and after a selective dorsal rhizotomy. Independent of the approach used (electromyography-informed versus static optimization), selective dorsal rhizotomy significantly normalized forces in spastic muscles during walking and did not reduce the contribution of non-spastic muscles. This study showed that selective dorsal rhizotomy improves dynamic muscle forces in children with cerebral palsy and leads to less gait pathology, as shown in the improvement in joint kinematics and joint kinetics. In idual muscle force analyses using the Muscle-Force-Profile extend standard joint kinematics and joint moment analyses, which might improve clinical-decision making in children with cerebral palsy in the future. The reference data of our participants and MATLAB code for the Muscle-Force-Profile are publicly available on rojects/muscleprofile.
Publisher: Frontiers Media SA
Date: 18-02-2020
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.GAITPOST.2019.05.008
Abstract: Performance of the sit-to-stand (STS) task is compromised in in iduals with advanced hip osteoarthritis (OA). Understanding how STS performance is altered in in iduals with mild-to-moderate hip OA may inform interventions to improve function and slow disease progression. Do trunk, pelvis, and hip biomechanics differ during a STS task between in iduals with mild-to-moderate hip OA and a healthy, age-matched control group? Thirteen in iduals with mild-to-moderate symptomatic and radiographic hip OA and seventeen healthy, age-matched controls performed a standardized STS task. Data were acquired using a three-dimensional motion capture system. The primary outcome measures were task duration, sagittal and frontal plane trunk, pelvis, and hip joint angles, and sagittal and frontal plane trunk and hip joint moments. Comparisons of lower-limb measures were between the most affected side in the hip OA group and a randomly chosen limb for the control group, termed the index limb, prior to and following lift-off from the chair. Participants with mild-to-moderate hip OA took longer to perform the STS task compared to controls. Prior to lift-off, the hip OA group exhibited greater posterior pelvic tilt, greater pelvic rise on the index side and less hip joint flexion relative to controls. Following lift-off, the hip OA group exhibited greater pelvic rise on the index side compared to controls. In iduals with mild-to-moderate hip OA exhibit subtle alterations in movement strategy compared to healthy controls when completing a STS task similar, to a small extent, to adaptations reported in advanced stages of the disease. Interventions to target these features and prevent further decline in physical function may be warranted in the management of mild-to-moderate hip OA while the opportunity remains.
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.JBIOMECH.2018.08.023
Abstract: In-vivo hip joint contact forces (HJCF) can be estimated using computational neuromusculoskeletal (NMS) modelling. However, different neural solutions can result in different HJCF estimations. NMS model predictions are also influenced by the selection of neuromuscular parameters, which are either based on cadaveric data or calibrated to the in idual. To date, the best combination of neural solution and parameter calibration to obtain plausible estimations of HJCF have not been identified. The aim of this study was to determine the effect of three electromyography (EMG)-informed neural solution modes (EMG-driven, EMG-hybrid, and EMG-assisted) and static optimisation, each using three different parameter calibrations (uncalibrated, minimise joint moments error, and minimise joint moments error and peak HJCF), on the estimation of HJCF in a healthy population (n = 23) during walking. When compared to existing in-vivo data, the EMG-assisted mode and static optimisation produced the most physiologically plausible HJCF when using a NMS model calibrated to minimise joint moments error and peak HJCF. EMG-assisted mode produced first and second peaks of 3.55 times body weight (BW) and 3.97 BW during walking static optimisation produced 3.75 BW and 4.19 BW, respectively. However, compared to static optimisation, EMG-assisted mode generated muscle excitations closer to recorded EMG signals (average across hip muscles R
Publisher: Elsevier BV
Date: 07-2020
Publisher: Elsevier BV
Date: 07-2012
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.JBIOMECH.2018.11.042
Abstract: Abnormal hip joint contact forces (HJCF) are considered a primary mechanical contributor to the progression of hip osteoarthritis (OA). Compared to healthy controls, people with hip OA often present with altered muscle activation patterns and greater muscle co-contraction, both of which can influence HJCF. Neuromusculoskeletal (NMS) modelling is non-invasive approach to estimating HJCF, whereby different neural control solutions can be used to estimate muscle forces. Static optimisation, available within the popular NMS modelling software OpenSim, is a commonly used neural control solution, but may not account for an in idual's unique muscle activation patterns and/or co-contraction that are often evident in pathological population. Alternatively, electromyography (EMG)-assisted neural control solutions, available within CEINMS software, have been shown to account for in idual activation patterns in healthy people. Nonetheless, their application in people with hip OA, with conceivably greater levels of co-contraction, is yet to be explored. The aim of this study was to compare HJCF estimations using static optimisation (in OpenSim) and EMG-assisted (in CEINMS) neural control solutions during walking in people with hip OA. EMG-assisted neural control solution was more consistent with both EMG and joint moment data than static optimisation, and also predicted significantly higher HJCF peaks (p < 0.001). The EMG-assisted neural control solution also accounted for more muscle co-contraction than static optimisation (p = 0.03), which probably contributed to these higher HJCF peaks. Findings suggest that the EMG-assisted neural control solution may estimate more physiologically plausible HJCF than static optimisation in a population with high levels of co-contraction, such as hip OA.
Publisher: Human Kinetics
Date: 10-2017
Abstract: Gait analysis together with musculoskeletal modeling is widely used for research. In the absence of medical images, surface marker locations are used to scale a generic model to the in idual’s anthropometry. Studies evaluating the accuracy and reliability of different scaling approaches in a pediatric and/or clinical population have not yet been conducted and, therefore, formed the aim of this study. Magnetic resonance images (MRI) and motion capture data were collected from 12 participants with cerebral palsy and 6 typically developed participants. Accuracy was assessed by comparing the scaled model’s segment measures to the corresponding MRI measures, whereas reliability was assessed by comparing the model’s segments scaled with the experimental marker locations from the first and second motion capture session. The inclusion of joint centers into the scaling process significantly increased the accuracy of thigh and shank segment length estimates compared to scaling with markers alone. Pelvis scaling approaches which included the pelvis depth measure led to the highest errors compared to the MRI measures. Reliability was similar between scaling approaches with mean ICC of 0.97. The pelvis should be scaled using pelvic width and height and the thigh and shank segment should be scaled using the proximal and distal joint centers.
No related grants have been discovered for Hoa Hoang.