Prey animals have evolved a wide variety of behaviours to combat the threat of predation, and these have been generally well studied. However, one of the most common and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental attention: so-called 'protean' behaviour. This is behaviour that is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey. In this study, we used human 'predators' participating in 3D virtual reality simulations to test how protean (i.e. unpredictable) variation in prey movement affects participants' ability to visually target them as they move (a key determinant of successful predation). We found that targeting accuracy was significantly predicted by prey movement path complexity, although, surprisingly, there was little evidence that high levels of unpredictability in the underlying movement rules equated directly to decreased predator performance. Instead, the specific movement rules differed in how they impacted on targeting accuracy, with the efficacy of protean variation in one element depending on the values of the remaining elements. These findings provide important insights into the understudied phenomenon of protean antipredator behaviour, which are directly applicable to predator-prey dynamics within a broad range of taxa.

Virtual Reality (VR) holds the promise of providing engaging embodied experiences, but little is known about how people with disabilities engage with it. We explore challenges and opportunities of VR gaming for wheelchair users. First, we present findings from a survey that received 25 responses and gives insights into wheelchair users' motives to (non-) engage with VR and their experiences. Drawing from this survey, we derive design implications which we tested through implementation and qualitative evaluation of three full-body VR game prototypes with 18 participants. Our results show that VR gaming engages wheelchair users, though nuanced consideration is required for the design of embodied immersive experiences for minority bodies, and we illustrate how designers can create meaningful, positive experiences.

Agent-based crowd simulations are used for modelling building and space usage, allowing designers to explore hypothetical real-world scenarios, including extraordinary events such as evacuations. Existing work which engages virtual reality (VR) as a platform for crowd simulations has been primarily focussed on the validation of simulation models through observation; the use of interactions such as gaze to enhance a sense of immersion; or studies of proxemics. In this work, we extend previous studies of proxemics and examine the effects of varying crowd density on user experience and behaviour. We have created a simulation in which participants walk freely and perform a routine manual task, whilst interacting with agents controlled by a typical social force simulation model. We examine and report the effects of crowd density on both affective state and behaviour. Our results show a significant increase in negative affect with density, measured using a self-report scale. We further show significant differences in some aspects of user behaviours, using video analysis, and discuss how our results relate to VR simulation design for mixed human-agent scenarios.

Abstract-In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our nodegraph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a "nearest-beacon" approach as a base-line; exponentially averaged weighted range estimates; and a particlefilter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilities.

This paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the conversion into labelled objects using a connected component labelling algorithm. The background models are based on 24-bit RGB values and 8-bit greyscale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The realtime connected component labelling algorithm, also designed for FPGA implementation, has efficiently been integrated with the pixel level background subtraction to extract pixels of a moving object as a single blob. The connected component algorithm, run-length encodes the binary image output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels.