Fun and social affiliation are good predictors of long-term intention to use game-based interventions including those for motivating physical activity, yet current player matching algorithms are poor at facilitating social connectedness. In this paper, we report on the results of a study investigating how different player traits are associated with interest in social features of an exercise game for improving player experience through better player matching using common and complementary characteristics. Twelve conceptual scenarios were illustrated using storyboards and data was collected from 196 respondents who rated their attitudes and preferences towards gamification elements. Correlational results showed that all scenarios, except for cutting corners, were perceived as persuasive, enjoyable, engaging and are likely to increase future exercise intention for players who score high on Philanthropist- and Socialiser-oriented traits. Results also showed that many players favour the altruistic donation feature. Furthermore, qualitative results underscore that it is the player's partner that matters more than the players’ personalities. We conclude with practical recommendations for designing more personalised exercise games that can include more socially engaging game mechanics in the future.

In this research, using storyboard implementation, we investigate the relationship between social game elements and the Hexad user type model for motivating exercise and play. The goal is to see if and how different gamer types are associated with interest in social features and if they can be used as the basis for player compatibility matching (similar or complementary). Participants (N = 60) rated the level of perceived persuasiveness, enjoyment, social engagement, and intention for future play on 8 social elements for step-based asynchronous exergames. Preliminary results show a significant positive relationship between social game elements and player types. Correlational analyses demonstrated that the gamification elements “Sharing Knowledge”, “Supportive Updates”, “Rotating Leadership” and “Leaderboard” were most strongly related to the “Philanthropist” player type. We conclude with recommendations for designing personalized multiplayer mobile exergames.

Gamification and exergames in particular have been broadly employed in health and fitness as an attempt to promote exercise and more active life styles. Motivated by popularity and availability of wearable activity trackers, we present the design and findings of a study on the motivational effects of using activity tracker-based games to promote daily exercise. Furthermore, we have investigated user behaviors, usage patterns, engagement, and parameters that affect them. An exergame was developed with an accompanying wearable device, for which different variations of application updates were pushed out periodically over a 70-day period. The results of this long-term study show that the usage of wearable activity trackers during exercise, even when gamified for increased entertainment, sees a consistent decline over time. This decline, however, is observed to be reversible with periodic updates to the game. This work, we believe, can make a significant contribution to solving the user retention problem of wearable-based exergames.

Interactive gaming has demonstrated promise as a low-cost, at-home training and fitness instruction alternative. Gaming systems offer convenience and the ability to provide enhanced reporting and progress data if body measurement information is collected effectively. However, commercially available systems today are designed primarily for entertainment and as a result, the quality of instruction delivery and level of involvement may not meet the needs of a user performing a disciplined activity.

This paper presents a new method for obstacle detection using optical flow. The method employs a highly efficient and accurate adaptive motion detection algorithm for determining the regions in the image which are more likely to contain obstacles. These regions then have optical flow performed on them. We call this method targeted optical flow. Targeted optical flow performs significantly faster compared to regular optical flow. We employ two types of optical flow to demonstrate the performance and speed increase of the proposed system. Finally, k-means clustering is employed for obstacle reconstruction. The system is designed for color videos for better performance. Several benchmark and recorded sequences have been used for testing the system.

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.

In this paper, we present a super-resolution (SR) technique for images having arbitrarily-shaped local illumination changes. These variations tend to degrade the performance of the image registration, and hence impact the SR image reconstruction. Conventional SR techniques focus on enhancing the reconstruction step assuming aligned images. In this paper, we exploit our recent registration approach of images having illumination variations using a robust bisquare M-estimation. Then, we extend a bounded total variation-based approach for upsampling single frames to super-resolving multi-frames in order to reconstruct the unknown high-resolution (HR) frame. The proposed SR technique shows clear improvements over competing techniques in terms of objective metrics using simulated and real image pairs with illumination variations.

In this paper, we present a registration approach for images having arbitrarily-shaped locally variant illuminations. These variations tend to degrade the performance of geometric registration precision (GRP) and impact subsequent processing. Traditional registration approaches typically use a least-squares estimator that is sensitive to outliers. Instead, we propose using a robust bisquare M-estimator, as it differently penalizes the small and large residuals. The proposed approach shows clear improvements over competing approaches in terms of GRP and illumination correction, using simulated and real image pairs.

In this paper, we focus on the sub-pixel geometric registration of images with arbitrarily-shaped local intensity variations, particularly due to shadows. Intensity variations tend to degrade the performance of geometric registration, thereby degrading subsequent processing. To handle intensity variations, we propose a model with illumination correction that can handle arbitrarily-shaped regions of local intensity variations. The approach is set in an iterative coarse-to-fine framework with steps to estimate the geometric registration with illumination correction and steps to refine the arbitrarily-shaped local intensity regions. The results show that this model outperforms linear scalar model by a factor of 6.8 in sub-pixel registration accuracy.

A modular approach to finding fast SIFT correspondences in single-image matching applications is proposed. Our algorithm exploits properties of the SIFT descriptor vector to find shortcuts to the most likely matches in a feature set. We are able to converge approximately 15 times faster than a linear search, and, respectively, four and five times faster than both PCA-SIFT and SURF (both of which use descriptor vectors that contain far fewer dimensions than SIFT), at near-equivalent recall and precision performance.