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Passmore, E.

Publications and source records attributed to Passmore, E..

2 recordsLinked to original sources

Joint loading in the presence of torsional deformities is overestimated unless gait adaptations are considered: a predictive simulation approach

Lower-limb torsional deformities have been shown to alter joint loading, although the findings vary between studies perhaps due to the simulation approaches applied. This study used predictive simulations to investigate how femoral neck anteversion (FNA) and external tibial torsion (ETT) influence hip and knee joint loading. Musculoskeletal models with altered FNA (1{degrees}-48{degrees}) and ETT (12{degrees}-53{degrees}), in isolation and combination, were created from a scaled adult model, and predictive walking (speed: 1.33 m/s) simulations were generated. Predictive simulations reproduced adaptations in hip rotation and foot-progression angle reported in individuals with torsional deformities. Hip and knee compressive, shear, and resultant contact forces were estimated, and multiple linear regressions quantified the independent associations of FNA and ETT with each outcome. Regressions accounted for 23%-86% of the variance in hip loading and 5%-87% in knee loading. FNA generally made the largest relative contribution to the variance explained in joint loading across regression models, although its associations varied in direction. Each 10{degrees} increase in FNA reduced the first hip compressive and resultant peaks by 0.076 and 0.035 BW, respectively, while hip shear force showed the largest increase, averaging 0.055 BW across both peaks. Most knee loads also increased, by up to 0.131 BW for the second resultant peak. Associations with ETT were primarily observed at the second peak. Our findings suggest that considering gait adaptations due to torsional alterations is crucial for estimating lower-limb joint loading, and prescribing joint kinematics and external forces while altering lower-limb torsion might lead to overestimation.

bioengineering↗

Quantifying spontaneous infant movements using state-space models

Over the first few months after birth, the typical emergence of spontaneous, fidgety general movements is associated with later developmental outcomes. In contrast, the absence of fidgety movements is a core feature of several neurodevelopmental and cognitive disorders. Currently, manual assessment of early infant movement patterns is time consuming and labour intensive, limiting its wider use. Recent advances in computer vision and deep learning have led to the emergence of pose estimation techniques, computational methods designed to locate and track body points from video without specialised equipment or markers, for movement tracking. In this study, we use automated markerless tracking of infant body parts to build statistical models of early movements. Using autoregressive, state-space models we demonstrate that infant movement can be modelled as a sequence of motor states, each characterised by specific body part movements, with expression that varies with age and differs in infants at high-risk of poor neurodevelopmental outcome.

bioengineering↗