Playing games has been shown to be an effective method of postwork recovery.Previous research has shown that gameplay with high cognitive involvement is effective for recovery.This finding conflicts with models of mental workload (MWL), which suggest that people feel best when cycling between high and low MWL.To unpack the relationship between recovery and mental workload, we designed a lab experiment where 40 participants experienced different combinations of high and low MWL while undertaking both work tasks and recovery gameplay, and we collected both selfreport and physiological (fNIRS) data.Results showed that high and low MWL games created different impacts on recovery, depending on the MWL of the prior work task.While fNIRS measurements of MWL varied as expected during work tasks, experience of MWL when playing games was not evident in the prefrontal cortex.We conclude by discussing the relationship between mental workload and theories of recovery.

Virtual Reality (VR) applications commonly use the illusion of self-motion (vection) to simulate experiences such as running, driving, or flying. However, this can lead to cybersickness, which diminishes the experience of users, and can even lead to disengagement with this platform. In this paper we present a study in which we show that users performing a cognitive task while experiencing a VR rollercoaster reported reduced symptoms of cybersickness. Furthermore, we collected and analysed brain activity data from our participants during their experience using functional near infra-red spectroscopy (fNIRS): preliminary analysis suggests the possibility that this technology may be able to detect the experience of cybersickness. Together, these results can assist the creators of VR experiences, both through mitigation of cybersickness in the design process, and by better understanding the experiences of their users.

Movement-based video games can provide engaging play experiences, and also have the potential to encourage physical activity. However, existing design guidelines for such games overwhelmingly focus on non-disabled players. Here, we explore wheelchair users’ perspectives on movement-based games as an enjoyable play activity. We created eight game concepts as discussion points for semi-structured interviews (N=6) with wheelchair users, and used Interpretative Phenomenological Analysis to understand their perspectives on physical activity and play. Themes focus on independent access, challenges in social settings, and the need for comprehensive adaptation. We also conducted an online survey (N=21) using the same game concepts, and thematic analysis highlighted the importance of adequate challenge, and considerations around multiplayer experiences. Based on these findings, we re-contextualize and expand guidelines for movement-based games previously established by Mueller and Isbister to include disabled players, and suggest design strategies that take into account their perspectives on play.

Cubic projection can be utilized to divide 360° images into multiple rectilinear images, with little distortion. However, the existing saliency prediction models fail to integrate semantic information of these images. In this paper, we address this by proposing an intra- and inter-reasoning graph convolutional network for saliency prediction on 360° images (SalReGCN360). The whole framework contains six sub-networks, each of which contains two branches. In the training phase, after utilizing Multiple Cubic Projection (MCP), six rectilinear images are simultaneously put into corresponding sub-networks. In one of the branches, the global features of a single rectilinear image are extracted by the intra-graph inference module to finely predict local saliency of 360° images. In the other branch, the contextual features are extracted by the inter-graph inference module to effectively integrate semantic information of six rectilinear images. Finally, the feature maps are generated by the two branches fusion, and six corresponding rectilinear saliency maps are predicted. Extensive experiments on two popular saliency datasets illustrate the superiority of the proposed model, especially the improvement in KLD metric.

Stereo super-resolution (SR) aims to enhance the spatial resolution of one camera view using additional information from the other. Previous deep-learning-based stereo SR methods indeed improved the SR performance effectively by employing additional information, but they are unable to super-resolve stereo images where there are large disparities, or different types of epipolar lines. Moreover, in these methods, one model can only super-solve images of a particular view, and for one specific scale factor. This paper proposes a cross parallax attention stereo super-resolution network (CPASSRnet) which can perform stereo SR of multiple scale factors for both views, with a single model. To overcome the difficulties of large disparity and different types of epipolar lines, a cross parallax attention module (CPAM) is presented, which captures the global correspondence of additional information for each view, relative to the other. CPAM allows the two views to exchange additional information with each other according to the generated attention maps. Quantitative and qualitative results compared with the state of the arts illustrate the superiority of CPASSRnet. Ablation experiments demonstrate that the proposed components are effective and noise tests verify the robustness of CPASSRnet.

The use of virtual reality (VR) to simulate confrontational human behaviour has significant potential for use in training, where the recreation of uncomfortable feelings may help users to prepare for challenging real-life situations. In this paper we present a user study (n=68) in which participants experienced simulated confrontational behaviour performed by a virtual character either in immersive VR, or on a 2D display. Participants reported a higher elevation in anxiety in VR, which correlated positively with a perceived sense of physical space. Character believability was influenced negatively by visual elements of the simulation, and positively by behavioural elements, which complements findings from previous work. We recommend the use of VR for simulations of confrontational behaviour, where a realistic emotional response is part of the intended experience. We also discuss incorporation of domain knowledge of human behaviours, and carefully crafted motion-captured sequences, to increase users’ sense of believability.

Researchers and developers have suggested that the use of diegetic interfaces can enhance users' sense of presence and immersion in virtual reality (VR) applications. While concepts of diegetic interfaces in VR are analogous to those seen on 2D displays, little work has considered how they might integrate with the movement-based controllers commonly used in consumer VR systems, to create higher fidelity interactions. In this paper we present a study (N = 58) in which we compare participants' experiences of diegetic and non-diegetic tool management interfaces, in a prototype VR crime scene investigation (CSI) training application. Contrary to expectations, we do not find evidence that participants' sense of presence is elevated when using the diegetic interface; however, we suggest that this may be due to reported higher levels of perceived workload, which can act to degrade user experience and engagement. We conclude by discussing the relationship between diegetic interface design and interaction fidelity, and highlighting trade-offs between fidelity, engagement, and learning outcomes in VR training applications.

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.

Playful technology has the potential to support physical activity (PA) among wheelchair users, but little is known about design considerations for this audience, who experience significant access barriers. In this paper, we lever-age the Integrated Behavioural Model (IBM) to understand wheelchair users' perspectives on PA, technology, and play.First, we present findings from an interview study with eight physically active wheelchair users. Second, we build on the interviews in a survey that received 44 responses from a broader group of wheelchair users. Results show that the anticipation of positive experiences was the strongest predictor of engagement with PA, and that accessibility concerns act as barriers both in terms of PA participation and technology use. We present four design goals - emphasizing enjoyment,involving others, building knowledge and enabling flexibility - to make our findings actionable for researchers and designers wishing to create accessible playful technology to support PA.

Gamification is widely applied to increase user engagement and motivation, but empirical studies on effectiveness are inconclusive, and often limited to the integration of tangible elements such as leaderboards or badges. In this paper, we report findings from a study with 36 participants that uses the lens of Self-Determination Theory to compare traditional gamification elements, and the concept of juiciness (the provision of abundant audiovisual feedback) in the VR simulation Predator!. Results show that gamification and juiciness improve user experience, but that only juiciness fulfills all basic psychological needs that facilitate intrinsic motivation when applied in nongaming settings. User preferences favour the combination of both approaches, however, neither improved performance, and there is evidence of juicy elements influencing user behaviour. We discuss implications of these findings for the integration of gamification, reflect on the role of both approaches in the context of feedback, and outline challenges and opportunities for further research.

The automated analysis of interacting objects or people from video has many uses, including the recognition of activities, and the identification of prototypical or unusual behaviors. Existing techniques generally use temporal sequences of quantifiable real-valued features, such as object position or orientation; however, more recently, qualitative representations have been proposed. In this paper, we present a novel and robust qualitative method, which can be used for both the classification and the clustering of pair-activities. We use qualitative trajectory calculus (QTC) to represent the relative motion between two objects and encode their interactions as a trajectory of QTC states. A key element is a general and robust means of determining the sequence similarity, which we term Normalized Weighted Sequence Alignment; we show that this is an effective metric for both recognition and clustering problems. We have evaluated our method across three different data sets, and have shown that it outperforms the state-of-the-art quantitative methods, achieving an error rate of no more than 4.1% for recognition, and cluster purities higher than 90%. Our motivation originates from an interest in automated analysis of animal behaviors, and we present a comprehensive video data set of fish behaviors (Gasterosteus aculeatus), collected from lab-based experiments.

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 introduce our node-graph model of user location, which is designed to represent the location layout. We also present our experimental work which includes 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 particle-filter 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 presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.

This paper introduces a tri-state logic self-organizing map (bSOM) designed and implemented on a field programmable gate array (FPGA) chip. The bSOM takes binary inputs and maintains tri-state weights. A novel training rule is presented. The bSOM is well suited to FPGA implementation, trains quicker than the original self-organizing map (SOM), and can be used in clustering and classification problems with binary input data. Two practical applications, character recognition and appearance-based object identification, are used to illustrate the performance of the implementation. The appearance-based object identification forms part of an end-to-end surveillance system implemented wholly on FPGA. In both applications, binary signatures extracted from the objects are processed by the bSOM. The system performance is compared with a traditional SOM with real-valued weights and a strictly binary weighted SOM.

Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution [1]. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved.

Methods of segmenting objects of interest from video data typically use a background model to represent an empty, static scene. However, dynamic processes in the background, such as moving foliage and water, can act to undermine the robustness of such methods and result in false positive object detections. Techniques for reducing errors have been proposed, including Markov Random Field (MRF) based pixel classification schemes, and also the use of region-based models. The work we present here combines these two approaches, using a region-based background model to provide robust likelihoods for multi-class MRF pixel labelling. Our initial results show the effectiveness of our method, by comparing performance with an analogous per-pixel likelihood model.

This paper introduces a real-time connected component labelling algorithm designed for field programmable gate array (FPGA) implementation. The algorithm run-length encodes the image, 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 runs are typically less than the number of pixels. The architecture is designed mainly on Block RAM (i.e. internal RAM) of the FPGA. A comparison with the multi-pass algorithm in hardware and software is presented to show the advantages of the algorithm. The algorithm runs comfortably in real-time with reasonably low resource utilization, making integration with other real-time algorithms feasible.

We present an integrated pixel segmentation and region tracking algorithm, designed for indoor environments. Visual monitoring systems often use frame differencing techniques to independently classify each image pixel as either foreground or background. Typically, this level of processing does not take account of the global image structure, resulting in frequent misclassification. We use an adaptive Gaussian mixture model in colour and space to represent background and foreground regions of the scene. This model is used to probabilistically classify observed pixel values, incorporating the global scene structure into pixel-level segmentation. We evaluate our system over 4 sequences and show that it successfully segments foreground pixels and tracks major foreground regions as they move through the scene.