William S P Robertson

Validation of an OpenSim full-body model with detailed lumbar spine for estimating lower lumbar spine loads during symmetric and asymmetric lifting tasks

Publisher: Informa UK Limited

Date: 04-02-2019

DOI: 10.1080/10255842.2018.1564819

Abstract: There is currently no validated full-body lifting model publicly available on the OpenSim modelling platform to estimate spinal loads during lifting. In this study, the existing full-body-lumbar-spine model was adapted and validated for lifting motions to produce the lifting full-body model. Back muscle activations predicted by the model closely matched the measured erector spinae activation patterns. Model estimates of intradiscal pressures and in vivo measurements were strongly correlated. The same spine loading trends were observed for model estimates and reported vertebral body implant measurements. These results demonstrate the suitability of this model to evaluate changes in lumbar loading during lifting.

The true potential of nonlinear stiffness for point absorbing wave energy converters

Publisher: Elsevier BV

Date: 02-2022

DOI: 10.1016/J.OCEANENG.2021.110342

Optimization of the Magnetic Field Produced by Frustum Permanent Magnets for Single Magnet and Planar Halbach Array Configurations

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2021

DOI: 10.1109/TMAG.2021.3085108

Boundary-Free Analytic Magnetic Field Calculations Including Soft Iron and Permanent Magnets Using an Iterative Discretization Technique

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2021

DOI: 10.1109/TMAG.2020.3010566

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Publisher: MDPI AG

Date: 23-03-2023

DOI: 10.3390/S23073392

Abstract: Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior s le per ID to compare with each query s le. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases.

Intra-Stroke Profiling of Wheelchair Propulsion Using Inertial Measurement Units

Publisher: MDPI

Date: 23-02-2018

DOI: 10.3390/PROCEEDINGS2060256

Injury Prevention of Elite Wheelchair Racing Athletes Using Simulation Approaches

Publisher: MDPI

Date: 23-02-2018

DOI: 10.3390/PROCEEDINGS2060255

Nonlinear stiffness enhancement of submerged wave energy device in high fidelity model

Publisher: Elsevier BV

Date: 06-2022

DOI: 10.1016/J.OCEANENG.2022.111295

Using a Robust Design Approach to Optimize Chair Set-up in Wheelchair Sport

Publisher: MDPI

Date: 11-02-2018

DOI: 10.3390/PROCEEDINGS2060482

University Of Adelaide

Location: Australia

View Organisation

Linkage Projects - Grant ID: LP190100419

Start Date: 07-2020

End Date: 06-2024

Amount: $115,028.00

Funder: Australian Research Council