The growth in the popularity of interactive network games has increased the importance of a better understanding of the effects of packet loss and latency on user performance. While previous work on network games has studied user tolerance for high latencies and has studied the effects of latency on user performance in real-time strategy games, to the best of our knowledge, there has been no systematic study of the effects of loss and latency on user performance. In this paper we study user performance for Unreal Tournament 2003 (UT2003), a popular first person shooter game, under varying amounts of packet loss and latency. First, we deduced typical real world values of packet loss and latency experienced on the Internet by monitoring numerous operational UT2003 game servers. We then used these deduced values of loss and latency in a controlled networked environment that emulated various conditions of loss and latency, allowing us to monitor UT2003 at the network, application and user levels. We designed maps that isolated the fundamental first person shooter interaction components of movement and shooting, and conducted numerous user studies under controlled network conditions. We find that typical ranges of packet loss have no impact on user performance or on the quality of game play. The levels of latency typical for most UT2003 Internet servers, while sometimes unpleasant, do not significantly affect the outcome of the game. Since most first person shooter games typically consist of generic player actions similar to those that we tested, we believe that these results have broader implications.

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

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Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high-resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red-yellow-black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS-Memorial Health Center Wound Clinic (Worcester, MA) following an Institutional Review Board approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.

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