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Orthopaedic Proceedings
Vol. 105-B, Issue SUPP_16 | Pages 63 - 63
17 Nov 2023
Bicer M Phillips AT Melis A McGregor A Modenese L
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Abstract

OBJECTIVES

Application of deep learning approaches to marker trajectories and ground reaction forces (mocap data), is often hampered by small datasets. Enlarging dataset size is possible using some simple numerical approaches, although these may not be suited to preserving the physiological relevance of mocap data. We propose augmenting mocap data using a deep learning architecture called “generative adversarial networks” (GANs). We demonstrate appropriate use of GANs can capture variations of walking patterns due to subject- and task-specific conditions (mass, leg length, age, gender and walking speed), which significantly affect walking kinematics and kinetics, resulting in augmented datasets amenable to deep learning analysis approaches.

METHODS

A publicly available (https://www.nature.com/articles/s41597-019-0124-4) gait dataset (733 trials, 21 women and 25 men, 37.2 ± 13.0 years, 1.74 ± 0.09 m, 72.0 ± 11.4 kg, walking speeds ranging from 0.18 m/s to 2.04 m/s) was used as the experimental dataset. The GAN comprised three neural networks: an encoder, a decoder, and a discriminator. The encoder compressed experimental data into a fixed-length vector, while the decoder transformed the encoder's output vector and a condition vector (containing information about the subject and trial) into mocap data. The discriminator distinguished between the encoded experimental data from randomly sampled vectors of the same size. By training these networks jointly using the experimental dataset, the generator (decoder) could generate synthetic data respecting specified conditions from randomly sampled vectors. Synthetic mocap data and lower limb joint angles were generated and compared to the experimental data, by identifying the statistically significant differences across the gait cycle for a randomly selected subset of the experimental data from 5 female subjects (73 trials, aged 26–40, weighing 57–74 kg, with leg lengths between 868–931 mm, and walking speeds ranging from 0.81–1.68 m/s). By conducting these comparisons for this subset, we aimed to assess the synthetic data generated using multiple conditions.


Orthopaedic Proceedings
Vol. 105-B, Issue SUPP_16 | Pages 34 - 34
17 Nov 2023
Elliott M Rodrigues R Hamilton R Postans N Metcalfe A Jones R McGregor A Arvanitis T Holt C
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Abstract

Objectives

Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics.

Methods

Data was curated through calls to members of the OATech Network+ (https://www.oatechnetwork.org/). The requirements were 3D motion capture data from previous studies that related to analysing the biomechanics of knee OA, including participants with OA at any stage of progression plus healthy controls. As a minimum we required kinematic data of the lower limbs, plus associated kinetic data (i.e. ground reaction forces). Any additional, complementary data such as EMG could also be provided. Relevant ethical approvals had to be in place that allowed re-use of the data for other research purposes. The datasets were uploaded to a University hosted cloud platform. The database platform was developed using Javascript and hosted on a Windows server, located and managed within the department.


Orthopaedic Proceedings
Vol. 103-B, Issue SUPP_2 | Pages 37 - 37
1 Mar 2021
Kaufmann J McGregor A Phillips A
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Abstract

Objectives

Osteoporosis of the pelvis and femur is diagnosed in a high proportion of lower-limb amputees which carries an increased fracture risk and subsequently serious implications on mobility, physical dependency and morbidity. Through the development of biofidelic musculoskeletal and finite element (FE) models, we aim to determine the effect of lower-limb amputation on long-term bone remodelling in the hip and to understand the potential underpinning mechanisms for bone degradation in the younger amputee population.

Methods

Our models are patient specific and anatomically accurate. Geometries are derived from MRI-scans of one bilateral, above-knee, amputee and one body-matched control subject. Musculoskeletal modelling enables comparison of muscle and joint reaction-forces throughout gait. This provides the loading scenario implemented in FE. FE modelling demonstrates the effect of loading on the amputated limb via a prosthetic socket by comparing bone mechanical stimulation in amputee and control cases.


Orthopaedic Proceedings
Vol. 103-B, Issue SUPP_2 | Pages 50 - 50
1 Mar 2021
Favier C McGregor A Phillips A
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Abstract

OBJECTIVES

Bone health deterioration is a major public health issue. General guidelines for the limitation of bone loss prescribe a healthy lifestyle and a minimum level of physical activity. However, there is no specific recommendation regarding targeted activities that can effectively maintain lumbar spine bone health. To provide a better understanding of such influencing activities, a new predictive modelling framework was developed to study bone remodelling under various loading conditions.

METHODS

The approach is based on a full-body subject-specific musculoskeletal model [1] combined with structural finite element models of the lumbar vertebrae. Using activities recorded with the subject, musculoskeletal simulations provide physiological loading conditions to the finite element models which simulate bone remodelling using a strain-driven optimisation algorithm [2]. With a combination of daily living activities representative of a healthy lifestyle including locomotion activities (walking, stair ascent and descent, sitting down and standing up) and spine-focused activities involving twisting and reaching, this modelling framework generates a healthy bone architecture in the lumbar vertebrae. The influence of spine-focused tasks was studied by adapting healthy vertebrae to an altered loading scenario where only locomotion activities were performed.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_17 | Pages 26 - 26
1 Apr 2013
Buisson Y McGregor A Strutton P
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Introduction

Changes in central nervous system (CNS) pathways controlling trunk and leg muscles in patients with low back pain(LBP) and lumbar radiculopathy have been observed and this study investigated whether surgery impacts upon these changes in the long term.

Methods

80 participants were recruited into the following groups: 25 surgery(S), 20 chronic LBP(CH), 14 spinal injection(SI), and 21 controls(C). Parameters of corticospinal control were examined before, at 6, 26 and 52 weeks following lumbar decompression surgery and equivalent intervals. Electromyographic(EMG) activity was recorded from tibialis anterior(TA), soleus(SOL), rectus abdominis(RA), external oblique(EO) and erector spinae(ES) muscles at the T12&L4 levels in response to transcranial magnetic stimulation of the motor cortex. Motor evoked potentials (MEP) and cortical silent periods(cSP) recruitment curves(RC) were analysed.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_13 | Pages 32 - 32
1 Mar 2013
Gulati V McGregor A Bergmann J
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Gathering reliable information about joint movement during activities of daily living is of clinical interest. Here we present pilot data regarding a new wearable knee joint sensing system by comparing the outcomes of this device to a gold standard. Initial results show a complex, but repetitive pattern. These outcomes generate potential for future work.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_4 | Pages 34 - 34
1 Jan 2013
Borhani M Bull A McGregor A
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Background

The measurement of pelvic kinematics is key to the analysis of aberrant movement patterns of lower back, yet to date technical issues of skin artefacts, body composition and optical motion tracking sensor occlusion [1] are unresolved.

Methods

In this study, an alternative technical pelvic coordinate system to the standard right and left anterior superior iliac spine (ASIS) and posterior superior iliac spine (PSIS) is developed and evaluated in two healthy male subjects (slim and overweight). The alternative system consists of a cluster of 3 retro-reflective markers attached to the Sacrum, thus allowing position and motion of the pelvis to be measured. In order to use these technical markers a static trial must be performed. The ASISs were calibrated relative to the technical frame; and the anatomical frame of the pelvis was defined relative to the technical coordinate frame. Each participant completed 5 walking trials and the angular rotations of the two methods were investigated using Euler angles.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_4 | Pages 25 - 25
1 Jan 2013
McGregor A Doré C Morris T Morris S Jamrozik K
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Introduction

This study sought to determine whether the functional outcome of two common spinal operations could be improved by a programme of post-operative rehabilitation and/or an educational booklet each compared with usual care.

Methods

This was a multi-centre, factorial, randomised controlled trial on the post operative management of spinal surgery patients, with randomisation stratified by surgeon and operative procedure. The study compared the effectiveness of a rehabilitation programme and an education booklet for the postoperative management of patients undergoing discectomy or lateral nerve root decompression surgery, each compared with “usual care” using a 2 × 2 factorial design, randomising patient to four groups; rehabilitation-only, booklet-only, rehabilitation-plus-booklet, and usual care only. The primary outcome measure was the Oswestry Disability Index (ODI) at 12 months, with secondary outcomes including visual analogue scale measures of back and leg pain. An economic analysis was also performed.