Preparing for exams is an important yet stressful time for many students. Self-testing is known to be an effective preparation strategy, yet some students lack motivation to engage or persist in self-testing activities. Adding game elements to a platform supporting self-testing may increase engagement and, by extension, exam performance. We conduct a randomized controlled experiment (n=701) comparing the effect of two game elements -- a points system and a badge system -- used individually and in combination. We find that the badge system elicits significantly higher levels of voluntary self-testing activity and this effect is particularly pronounced amongst a relatively small cohort. Importantly, this increased activity translates to a significant improvement in exam scores. Our data supports a causal relationship between gamification and learning outcomes, mediated by self-testing behavior. This provides empirical support for Landers' theory of gamified learning when the gamified activity is conducted prior to measuring learning outcomes.

Abstract The ability to accurately achieve performance capture of athlete motion during competitive play in near real‐time promises to revolutionize not only broadcast sports graphics visualization and commentary, but also potentially performance analysis, sports medicine, fantasy sports and wagering. In this paper, we present a highly portable, non‐intrusive approach for synthesizing human athlete motion in competitive game‐play with lightweight instrumentation of both the athlete and field of play. Our data‐driven puppetry technique relies on a pre‐captured database of short segments of motion capture data to construct a motion graph augmented with interpolated motions and speed variations. An athlete's performed motion is synthesized by finding a related action sequence through the motion graph using a sparse set of measurements from the performance, acquired from both worn inertial and global location sensors. We demonstrate the efficacy of our approach in a challenging application scenario, with a high‐performance tennis athlete wearing one or more lightweight body‐worn accelerometers and a single overhead camera providing the athlete's global position and orientation data. However, the approach is flexible in both the number and variety of input sensor data used. The technique can also be adopted for searching a motion graph efficiently in linear time in alternative applications.

The ever increasing availability of high speed Internet access has led to a leap in technologies that support real-time realistic interaction between humans in online virtual environments. In the context of this work, we wish to realise the vision of an online dance studio where a dance class is to be provided by an expert dance teacher and to be delivered to online students via the web. In this paper we study some of the technical issues that need to be addressed in this challenging scenario. In particular, we describe an automatic dance analysis tool that would be used to evaluate a student's performance and provide him/her with meaningful feedback to aid improvement.

In this paper, we address the problem of reconstructing 3D volumetric models, illustrating human sporting performance for use in coaching scenarios. We advocate the use of low cost camera networks for acquiring such data, allowing the approach to be feasibly adopted by both amateur and elite level sports athletes. A dynamic voxel carving approach is described, coupled with over-head player tracking and autonomous background subtraction, to automatically produce a 3D reconstruction technique that intelligently uses memory resources. We demonstrate the efficacy of our approach in the context of tennis as a challenging application scenario.

In this work, we describe a novel system that automatically evaluates dance performances against a gold-standard performance and provides visual feedback to the performer in a 3D virtual environment. The system acquires the motion of a performer via Kinect-based human skeleton tracking, making the approach viable for a large range of users, including home enthusiasts. Unlike traditional gaming scenarios, when the motion of a user must by kept in synch with a pre-recorded avatar that is displayed on screen, the technique described in this paper targets online interactive scenarios where dance choreographies can be set, altered, practiced and refined by users. In this work, we have addressed some areas of this application scenario. In particular, a set of appropriate signal processing and soft computing methodologies is proposed for temporally aligning dance movements from two different users and quantitatively evaluating one performance against another.

The Huawei/3DLife Grand Challenge Dataset provides multimodal recordings of Salsa dancing, consisting of audiovisual streams along with depth maps and inertial measurements. In this paper, we propose a system for augmented reality-based evaluations of Salsa dancer performances. An essential step for such a system is the automatic temporal synchronisation of the multiple modalities captured from different sensors, for which we propose efficient solutions. Furthermore, we contribute modules for the automatic analysis of dance performances and present an original software application, specifically designed for the evaluation scenario considered, which enables an enhanced dance visualisation experience, through the augmentation of the original media with the results of our automatic analyses.

Whilst voxel carving approaches exist that allow non-invasive 3D human reconstruction, their performance is heavily dependent on the number of cameras used and the placement of these cameras around the subject. We present a technique to quantify the fall-off in accuracy of spatially carved volumetric representations of humans based on real world constraints. We describe an example of such a quantitative evaluation using a synthetic dataset of typical sports motion in a tennis court scenario, created using computer graphics techniques and motion capture data. Experiments are performed using a baseline voxel carving technique that includes player tracking, background subtraction and player voxel carving. This type of quantitative evaluation could be used by amateur sporting clubs without a sophisticated capture infrastructure to understand how best to instrument a camera network in order to obtain a good trade-off between reconstruction accuracy and installation cost.

Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals.

Colour permeates our daily lives, yet we rarely take notice of it. In this work we utilise the SenseCam (a visual lifelogging tool), to investigate the predominant colours in one million minutes of human life that a group of 20 individuals encounter throughout their normal daily activities. We also compare the colours that different groups of people are exposed to in their typical days. This information is presented in using a novel colour-wheel visualisation which is a new means of illustrating that people are exposed to bright colours over longer durations of time during summer months, and more dark colours during winter months.

Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been investigated using both traditional 2-D computer vision techniques and, more recently, 3-D stereo information. However, to date, a quantitative assessment of the performance of stereo-based pedestrian detection has been problematic, mainly due to the lack of standard stereo-based test data and an agreed methodology for carrying out the evaluation. This has forced researchers into making subjective comparisons between competing approaches. In this paper, we propose a framework for the quantitative evaluation of a short-baseline stereo-based pedestrian detection system. We provide freely available synthetic and real-world test data and recommend a set of evaluation metrics. This allows researchers to benchmark systems, not only with respect to other stereo-based approaches, but also with more traditional 2-D approaches. In order to illustrate its usefulness, we demonstrate the application of this framework to evaluate our own recently proposed technique for pedestrian detection and tracking.