Visualizing design exclusion predicted by disability data: a mobile phone case study

Disability data can help to predict the number of people that will be unable to use a particular product. The greatest benefits of this prediction are the design insights that help to reduce exclusion and thereby improve the product experience for a broad range of people. This paper uses a mobile phone case study to demonstrate how a set of visualization outputs from an exclusion audit can generate prioritized design insights to reduce exclusion, particularly when multiple tasks place demands on multiple capabilities

Gradually including potential users: A tool to counter design exclusions

The paper describes an iterative development process used to understand the suitability of different inclusive design evaluation tools applied into design practices. At the end of this process, a tool named Inclusive Design Advisor was developed, combining data related to design features of small appliances with ergonomic task demands, anthropometric data and exclusion data. When auditing a new design the tool examines the exclusion that each design feature can cause, followed by objective recommendations directly related to its features. Interactively, it allows designers or clients to balance design changes with the exclusion caused. It presents the type of information that enables designers and clients to discuss user needs and make more inclusive design decisions.We would like to thank the Engineering and Physical Sciences Research Council (grant number 972367) and the India-UK Advanced Technology Centre (IU-ATC) for supporting the project of which this paper is part

Using disability data to estimate design exclusion

Abstract Exclusion auditing is a process that can quantitatively evaluate the inclusive merit of different products, or alternative design decisions. The results from such an audit can provide prioritised directions for product improvement and support the business case for reducing the capability levels required to use mainstream products. The 1996/97 disability follow-up survey, conducted by the Office of National Statistics, is currently the most comprehensive data source for estimating design exclusion in the UK. The data source is explained in more detail, and a method presented that uses it to estimate the exclusion associated with several tasks that occur in series or parallel, illustrated through worked examples. Having evaluated how many people are excluded, one can investigate why they were excluded, thus generating design insights for how they could be included. Data from the survey is also converted to a series of stylized graphs, which are intended to inspire designers to think about the relationship between the demands required to use a product and the resulting levels of population exclusion.The research for this paper was funded from the EPSRC i~design project. Thanks to Nicholas Caldwell for help with programming and to Amanda Turner for providing the matrix interpretation of set theory

An inclusive design perspective on automotive HMI trends

This paper looks at recent trends in automotive human machine interfaces, with a lens of evaluation from an inclusive design perspective. The goal of Inclusive Design is to ensure that the population of potential users for a product or service is maximised. Until relatively recently, automotive human machine interfaces (HMI’s) have excluded and caused difficulties for users due to visibility, reach and force required to operate controls. Over the last 15 or so years however, there has been a significant increase in control and display location, interface types and integration of functions, as well as dramatically increased potential functionality due to in-vehicle emergent technologies. It is suggested that this increase in interface unfamiliarity for a driver will cause significant difficulty and potential exclusion, due to the demands of learning and conflicts in expectation. The effects on this trend in the context of an ageing population and automated driving technologies are discussed.This work was carried out at the University of Cambridge’s Engineering Design Centre, within the Inclusive Design Group. Sponsor: Jaguar Land Rover.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/978-3-319-40238-3_5

Visualising the number of people who cannot perform tasks related to product interactions

Understanding the number of people who cannot perform particular tasks helps to inform design decisions for mainstream products, such as the appropriate size and contrast of visual features. Making such informed decisions requires a dataset that is representative at the level of a national population, with sufficient scope and granularity to cover the types of actions associated with product use. Furthermore, visualisations are needed to bring the dataset to life, in order to better support comparing the number of people who cannot perform different tasks. The 1996/97 Disability Follow-up Survey remains the most recent Great British dataset to cover all types of ability losses that may be relevant to using everyday products. This paper presents new visualisations derived from this dataset, which are related to vision, hearing, cognition, mobility, dexterity and reach. Compared to previous publications on this dataset, the new visualisations contain task descriptions that have been simplified, described pictorially and separated out into different categories. Furthermore, two-dimensional visualisations are used to present exclusion results for products that require vision and/or hearing and for tasks that require each hand to do different things. In order to produce these new visualisations, the publicly available version of this dataset had to be reanalysed and recoded, which is described here-in detail.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10209-013-0297-

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach

A comparison of repetitive corrugation and straightening and high-pressure torsion using an Al-Mg-Sc alloy

A comparative study was conducted to evaluate the influence of two different severe plastic deformation (SPD) processes: repetitive corrugation and straightening (RCS) and high-pressure torsion (HPT). Samples of an Al-3Mg-0.25Sc alloy with an initial grain size of ∼150 μm were processed by RCS through 8 passes at room temperature either without any rotation during processing or with a rotation of 90° around the longitudinal axis between each pass. Thin discs of the alloy were also processed for up to 5 turns by HPT at room temperature. The results show that both procedures introduce significant grain refinement with average grain sizes of ∼0.6–0.7 μm after RCS and ∼95 nm after HPT. Measurements of the Vickers microhardness gave values of ∼128 after RCS and ∼156 after HPT. The results demonstrate that processing by HPT is the optimum processing technique in achieving both high strength and microstructural homogeneity

A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive pros-thetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD

Driver and Passenger Identification from Smartphone Data

The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics and Electronics (CAPE) agreement

Physical activity to improve cognition in older adults: can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis

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