The mobile arm support project as a test-bed for design research at the Cambridge EDC

A host of methods and tools to support designing are being developed in Cambridge EDC. These range from tools for design management to those for the generation and selection of design ideas, layouts, materials and production processes. A project, to develop a device to improve arm mobility of muscular dystrophy sufferers, is undertaken as a test-bed to evaluate and improve these methods and tools as well as to observe and modify its design and management processes. This paper presents the difficulties and advantages of using design methods and tools within this rehabilitation design context, with special focus on the evolution of the designs, tools, and management processes.

The use of spatial impulse responses to characterise flexible forming processes with mobile tools

A novel test method for the characterisation of flexible forming processes is proposed and applied to four flexible forming processes: Incremental Sheet Forming (ISF), conventional spinning, the English wheel and power hammer. The proposed method is developed in analogy with time-domain control engineering, where a system is characterised by its impulse response. The spatial impulse response is used to characterise the change in workpiece deformation created by a process, but has also been applied with a strain spectrogram, as a novel way to characterise a process and the physical effect it has on the workpiece. Physical and numerical trials to study the effects of process and material parameters on spatial impulse response lead to three main conclusions. Incremental sheet forming is particularly sensitive to process parameters. The English wheel and power hammer are strongly similar and largely insensitive to both process and material parameters. Spinning develops in two stages and is sensitive to most process parameters, but insensitive to prior deformation. Finally, the proposed method could be applied to modelling, classification of existing and novel processes, product-process matching and closed-loop control of flexible forming processes. © 2012 Elsevier B.V.

A population perspective on mobile phone related tasks

Chapter 6 A Population Perspective on Mobile Phone Related Tasks M. Bradley, S. Waller, J. Goodman-Deane, l. Hosking, R. Tenneti, PM Langdon and PJ Clarkson 6.1 Introduction For design to be truly inclusive, it needs to take into ...

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Generating the Sparse Point Cloud of a Civil Infrastructure Scene Using a Single Video Camera under Practical Constraints

Automating the model generation process of infrastructure can substantially reduce the modeling time and cost. This paper presents a method to generate a sparse point cloud of an infrastructure scene using a single video camera under practical constraints. It is the first step towards establishing an automatic framework for object-oriented as-built modeling. Motion blur and key frame selection criteria are considered. Structure from motion and bundle adjustment are explored. The method is demonstrated in a case study where the scene of a reinforced concrete bridge is videotaped, reconstructed, and metrically validated. The result indicates the applicability, efficiency, and accuracy of the proposed method.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

Evaluation of Setup Procedures on Mobile Devices Based on Users’ Initial Experience

Users’ initial perceptions of their competence are key motivational factors for further use. However, initial tasks on a mobile operating system (OS) require setup procedures, which are currently largely inconsistent, do not provide users with clear, visible and immediate feedback on their actions, and require significant adjustment time for first-time users. This paper reports on a study with ten users, carried out to better understand how both prior experience and initial interaction with two touchscreen mobile interfaces (Apple iOS and Google Android) affected setup task performance and motivation. The results show that the reactions to setup on mobile interfaces appear to be partially dependent on which device was experienced first. Initial experience with lower-complexity devices improves performance on higher-complexity devices, but not vice versa. Based on these results, the paper proposes six guidelines for designers to design more intuitive and motivating user interfaces (UI) for setup procedures. The preliminary results indicate that these guidelines can contribute to the design of more inclusive mobile platforms and further work to validate these findings is proposed.