Force sensing for measuring human body movement

The research developed in this thesis explores the sensing and inference of human movement in a dynamic way, as opposed to conventional measurement systems, that are only concerned with discrete evaluations of stimuli in sequential time. Typically, conventional approaches are used to infer the dynamic movement of the body; such as vision and motion tracking devices, with either a human diagnosis or complex image processing algorithm to classify the movement. This research is therefore the first of its kind to attempt and provide a movement classifying algorithm through the use of minimal sensing points, with the application for this novel system, to classify human movement during a golf swing. There are two main categories of force sensing. Firstly, array-type systems consisting of many sensing elements, and are the most commonly researched and commercially available. Secondly, reduced force sensing element systems (RFSES) also known as distributive systems have only been recently exploited in the academic world. The fundamental difference between these systems is that array systems handle the data captured from each sensor as unique outputs and suffer the effects of resolution. The effect of resolution, is the error in the load position measurement between sensing elements, as the output is quantized in terms of position. This can be compared to a reduced sensor element system that maximises that data received through the coupling of data from a distribution of sensing points to describe the output in discrete time. Also this can be extended to a coupling of transients in the time domain to describe an activity or dynamic movement. It is the RFSES that is to be examined and exploited in the commercial sector due to its advantages over array-based approaches such as reduced design, computational complexity and cost.

The knowledge competitiveness of regional economies: conceptualisation and measurement

Whereas the competitive advantage of firms can arise from size and position within their industry as well as physical assets, the pattern of competition in advanced economies has increasingly come to favour those firms that can mobilise knowledge and technological skills to create novelty in their products. At the same time, regions are attracting growing attention as an economic unit of analysis, with firms increasingly locating their functions in select regions within the global space. This article introduces the concept of knowledge competitiveness, defined as an economy’s knowledge capacity, capability and sustainability, and the extent to which this knowledge is translated into economic value and transferred into the wealth of the citizens. The article discusses the way in which the knowledge competitiveness of regions is measured and further introduces the World Knowledge Competitiveness Index, which is the first composite and relative measure of the knowledge competitiveness of the globe’s best performing regions.

Phakometric measurement of ocular surface radii of curvature, axial separations and alignment in relaxed and accommodated human eyes

Measurements (autokeratometry, A-scan ultrasonography and video ophthalmophakometry) of ocular surface radii, axial separations and alignment were made in the horizontal meridian of nine emmetropes (aged 20-38 years) with relaxed (cycloplegia) and active accommodation (mean ± 95% confidence interval: 3.7 ± 1.1 D). The anterior chamber depth (-1.5 ± 0.3 D) and both crystalline lens surfaces (front 3.1 ± 0.8 D; rear 2.1 ± 0.6 D) contributed to dioptric vergence changes that accompany accommodation. Accommodation did not alter ocular surface alignment. Ocular misalignment in relaxed eyes is mainly because of eye rotation (5.7 ± 1.6° temporally) with small amounts of lens tilt (0.2 ± 0.8° temporally) and decentration (0.1 ± 0.1 mm nasally) but these results must be viewed with caution as we did not account for corneal asymmetry. Comparison of calculated and empirically derived coefficients (upon which ocular surface alignment calculations depend) revealed that negligible inherent errors arose from neglect of ocular surface asphericity, lens gradient refractive index properties, surface astigmatism, effects of pupil size and centration, assumed eye rotation axis position and use of linear equations for analysing Purkinje image shifts. © 2004 The College of Optometrists.

Transverse load and orientation measurement with multicore fiber Bragg gratings

We demonstrate the sensitivity of Bragg gratings in a multicore fiber to transverse load. The Bragg peaks are split because of stress-induced birefringence, the magnitude of which depends upon the load and grating position relative to the load axis. Experiments show that a set of gratings in a four-core fiber can measure a load axis angle to ±5° and a load magnitude to ±15 N m-1 up to 2500 N m-1. We consider alternative designs of multicore fiber for optimal load sensing and compare experimental and modeled data. © 2005 Optical Society of America.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.