Publications by authors named "Israel Luis"

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled.

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Recent years have witnessed breakthroughs in assistive exoskeletons; both passive and active devices have reduced metabolic costs near preferred walking speed by assisting muscle actions. Metabolic reductions at multiple speeds should thus also be attainable. Musculoskeletal simulation can potentially predict the interaction between assistive moments, muscle-tendon mechanics, and walking energetics.

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The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle-tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature.

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Muscle-driven simulations have been widely adopted to study muscle-tendon behavior; several generic musculoskeletal models have been developed, and their biofidelity improved based on available experimental data and computational feasibility. It is, however, not clear which, if any, of these models accurately estimate muscle-tendon dynamics over a range of walking speeds. In addition, the interaction between model selection, performance criteria to solve muscle redundancy, and approaches for scaling muscle-tendon properties remain unclear.

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Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs.

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The assembly of a large-bandgap one-dimensional (1D) oxide-conductive carbon-chalcogenide nanocomposite and its surface, optical, and photoelectrochemical properties are presented. Microscopy, surface analysis, and optical spectroscopy results are reported to provide insights into the assembly of the nanostructure. We have investigated (i) how the various carbon allotropes (C60), reduced graphene oxide (RGO), carbon nanotubes (CNTs), and graphene quantum dots (GQDs) can be integrated at the interface of the 1D TiO2 and zero-dimensional (0D) CdS nanocrystals; (ii) the carbon allotrope and CdS loading effects; (iii) the impact of the carbon allotrope presence on 0D CdS nanocrystals; and (iv) how they promote light absorbance.

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