Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations is sequentially calculated by modeling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelization. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions.

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.

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Designers require knowledge and data about users to effectively evaluate product accessibility during the early stages of design. This paper addresses this problem by setting out the sensory, cognitive and motor dimensions of user capability that are important for product interaction. The relationship between user capability and product demand is used as the underlying conceptual model for product design evaluations and for estimating the number of people potentially excluded from using a given product.

Many products today are laden with a host of features which, for the majority of users, remain unused and often obscure the use of the simple features of use for which the product was devised (Norman in The design of everyday things. Basic Books, 2002; Keates and Clarkson in Countering design exclusion-an introduction to inclusive design. Springer, 2004). Since the cognitive capabilities of the marketed target group are largely not affected by age-related impairment, the intellectual demands of such products are frequently high (Rabbitt in Quart J Exp Psychol 46A(3): 1993). In addition, the age and technology generation of a product user will colour their expectations of the product interface and affect the range of skills they have available (Docampo in Technology generations handling complex User Interfaces. Ph. D. thesis, 2001). This paper addresses the issue of what features of products make them easy or difficult to learn to use, for the wider population as well as the older user, and whether and in what way individual prior experience affect the learning and use of a product design. To achieve the above, the interactions of users of varying ages and capabilities with two different everyday products were recorded in detail as they performed set tasks. Retrospective verbal protocols were then analysed using a category scheme based on an analysis of types of learning and cognition errors. This data was then compared with users' performance on individual detailed experience questionnaires and a number of tests of general and specific cognitive capabilities. The principal finding was that similarity of prior experience to the usage situation was the main determinant of performance, although there was also some evidence for a gradual, age-related capability decline. Users of all ages adopted a means-end or trial and error interaction when faced with unfamiliar elements of the interaction. There was a strong technology generation effect such that older users were reluctant or unable to complete the required tasks for a digital camera.Keywords Inclusive design Á Product design Á Cognition Á Training Á Working memory Experience and inclusive product designIn the context of demographic changes leading to an increasing number of older people, inclusive design research strives to relate the capabilities of the population to the design of products by effectively characterising the user. By 2020, almost half the adult population in the UK will be over 50, with the over 80s being the most rapidly growing sector. Recent research into inclusive design has investigated the relationship between the capabilities of the population at large; derived from statistical data sets, and properties and features of the design of products [1][2][3]. Products meeting the ideals of inclusive design aim to minimise the number of person who have difficulty with, or are excluded from use, or to control such exclusion by manipulation of product features [4,5]. A primary area of concern is with the cognitive capabilities of older users. In pa...

Saccadic accuracy requires that the control signal sent to the motor neurons must be the right size to bring the fovea to the target, whatever the initial position of the eyes (and corresponding state of the eye muscles). Clinical and experimental evidence indicates that the basic machinery for generating saccadic eye movements, located in the brainstem, is not accurate: learning to make accurate saccades requires cerebellar circuitry located in the posterior vermis and fastigial nucleus. How do these two circuits interact to achieve adaptive control of saccades? A model of this interaction is described, based on Kawato's principle of feedback-error-learning. Its three components were (1) a simple controller with no knowledge of initial eye position, corresponding to the superior colliculus; (2) Robinson's internal feedback model of the saccadic burst generator, corresponding to preoculomotor areas in the brain-stem; and (3) Albus's Cerebellar Model Arithmetic Computer (CMK), a neural net model of the cerebellum. The connections between these components were (I) the simple feedback controller passed a (usually inaccurate) command to the pulse generator, and (2) a copy of this command to the CMAC; (3) the CMAC combined the copy with information about initial eye position to (4) alter the gain on the pulse generator's internal feedback loop, thereby adjusting the size of burst sent to the motor neurons. (5) If the saccade were inaccurate, an error signal from the feedback controller adjusted the weights in the CMAC. It was proposed that connection (2) corresponds to the mossy fiber projection from superior colliculus to oculomotor vermis via the nucleus reticularis tegmenti pontis, and connection (5) to the climbing fiber projection from superior colliculus to the oculomotor vermis via the inferior olive. Plausible initialization values were chosen so that the system produced hypometric saccades (as do human infants) at the start of learning, and position-dependent hypermetric saccades when the cerebellum was removed. Simulations for horizontal eye movements showed that accurate saccades from any starting position could be learned rapidly, even if the error signal conveyed only whether the initial saccade were too large or too small. In subsequent tests the model adapted realistically both to simulated weakening of the eye muscles, and to intrasaccadic displacement of the target, thereby mimicking saccadic plasticity in adults. The architecture of the model may therefore offer a functional explanation of hitherto mysterious tectocerebellar projections, and a framework for investigating in greater detail how the cerebellum adaptively controls saccadic accuracy.

Using an in-vehicle interactive display, such as a touchscreen, typically entails undertaking a free hand pointing gesture and dedicating a considerable amount of attention, that can be otherwise available for driving, with potential safety implications. Due to road and driving conditions, the user input can also be subject to high levels of perturbations resulting in erroneous selections. In this article, we give an overview of the novel concept of an intelligent predictive display in vehicles. It can infer, notably early in the pointing task and with high confidence, the item the user intends to select on the display from the tracked free hand pointing gesture and possibly other available sensory data. Accordingly, it simplifies and expedites the target acquisition (pointing and selection), thereby substantially reducing the time and effort required to interact with an in-vehicle display. As well as briefly addressing the various signal processing and human factor challenges posed by predictive displays in the automotive environment, the fundamental problem of intent inference is discussed and a Bayesian formulation is introduced. Empirical evidence from data collected in instrumented cars is shown to demonstrate the usefulness and effectiveness of this solution.

Point and click" interactions remain one of the key features of graphical user interfaces (GUIs). People with motion-impairments, however, can often have difficulty with accurate control of standard pointing devices. This paper discusses work that aims to reveal the nature of these difficulties through analyses that consider the cursor's path of movement. A range of potential cursor measures was applied, and a number of them were found to be significant in capturing the differences between able-bodied users and motion-impaired users, as well as the differences between a haptic force feedback condition and a control condition. The cursor measures found in the literature, however, do not make up a comprehensive list, but provide a starting point for analysing cursor movements more completely. Six new cursor characteristics for motion-impaired users are introduced to capture aspects of cursor movement different from those already proposed.