Search

Emerging evidence has suggested that gluteus medius (GM) muscle activity may be critical for functional performance in individuals with chronic ankle instability (CAI). This study aimed to systematically review the literature to determine whether there are differences in GM muscle activity between individuals with and without CAI. A comprehensive search in PubMed, Embase, Cochrane Library, Web of Science, EBSCO, and PEDro databases was undertaken from the year of inception to 10 June 2024. Studies that investigated GM muscle activity during physical activities in healthy controls or copers and individuals with CAI were included. The quality assessment was conducted using the Newcastle-Ottawa Quality scale (NOS). After review, forty studies (1840 participants) were included; NOS scoring for the included studies ranged from 5/9 to 9/9 stars. GM activity was reported for seven activities: walking (14 studies), stance-transition (four studies), jump-landing (13 studies), perturbation (six studies), balance (four studies), cutting (three studies), and other functional exercises (seven studies). The outcome measures selected to examine each task varied across studies, and electromyography (EMG) results were inconsistent. Overall, although the quality of the available studies was generally high, there were substantial methodological differences, and the activity of GM muscles in CAI participants compared to controls was equivocal. A consensus on standardization of GM muscle activity assessment reporting should be established to guide future studies. [ABSTRACT FROM AUTHOR]

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part of NMS modelling is the estimation of musculotendon kinematics, which comprise musculotendon unit lengths, moment arms, and lines of action. Musculotendon kinematics, which are partially dependent on joint angles, define the non-linear mapping of muscle forces to joint moments and contact forces. Currently, real-time computation of musculotendon kinematics requires creation of a per-individual surrogate model. The computational speed and accuracy of these surrogates degrade with increasing number of coordinates. We developed a feed-forward neural network that completely encodes musculotendon kinematics of a target model across a wide anthropometric range, enabling accurate real-time estimates of musculotendon kinematics without need for a priori creation of a per-individual surrogate model. Compared to reference, the neural network had median normalized errors ~0.1% for musculotendon lengths, <0.4% for moment arms, and <0.10° for line of action orientations. The neural network was employed within an electromyogram-informed NMS model to calculate hip contact forces, demonstrating little difference (normalized root mean square error $1.23\pm 0.15$ %) compared to using reference musculotendon kinematics. Finally, execution time was <0.04 ms per frame and constant for increasing number of model coordinates. Our approach to musculoskeletal kinematics may facilitate deployment of complex real-time NMS modelling in computer vision or wearable sensors applications to realize biomechanics monitoring, rehabilitation, and disease management outside the research laboratory. [ABSTRACT FROM AUTHOR]

In this paper, a novel low leakage saddle junctionless field effect transistor with assistant gate is proposed. Its electrical properties have been extensively investigated by studying the influence resulting from variation of design parameters, such as the thickness of assistant gate, oxide layer thickness between the main and assistant gate, extension height of S/D contact, and the voltage of assistant gate. Compared with conventional structure, the proposed saddle junctionless field effect transistor with assistant gate shows much better performance at off state. The leakage current is effectively restrained, and the Ion‐Ioff ratio is largely improved. Design parameter optimization has also been performed. [ABSTRACT FROM AUTHOR]