Step synchronization and third person speed perception in virtual environment locomotion simulators

Human locomotion is known to be influenced by observation of another person's gait. For example, athletes often synchronize their step in long distance races. However, how interaction with a virtual runner affects the gait of a real runner has not been studied. We investigated this by creating an illusion of running behind a virtual model (VM) using a treadmill and large screen virtual environment showing a video of a VM. We looked at step synchronization between the real and virtual runner and at the role of the step frequency (SF) in the real runner's perception of VM speed. We found that subjects match VM SF when asked to match VM speed with their own (Figure 1). This indicates step synchronization may be a strategy of speed matching or speed perception. Subjects chose higher speeds when VMSF was higher (though VM was 12km/h in all videos). This effect was more pronounced when the speed estimate was rated verbally while standing still. (Figure 2). This may due to correlated physical activity affecting the perception of VM speed [Jacobs et al. 2005]; or step synchronization altering the subjects' perception of self speed [Durgin et al. 2007]. Our findings indicate that third person activity in a collaborative virtual locomotive environment can have a pronounced effect on an observer's gait activity and their perceptual judgments of the activity of others: the SF of others (virtual or real) can potentially influence one's perception of self speed and lead to changes in speed and SF. A better understanding of the underlying mechanisms would support the design of more compelling virtual trainers and may be instructive for competitive athletics in the real world. © 2009 ACM.

Insulin analog preparations and their use in children and adolescents with type 1 diabetes mellitus.

Standard or 'traditional' human insulin preparations such as regular soluble insulin and neutral protamine Hagedorn (NPH) insulin have shortcomings in terms of their pharmacokinetic and pharmacodynamic properties that limit their clinical efficacy. Structurally modified insulin molecules or insulin 'analogs' have been developed with the aim of delivering insulin replacement therapy in a more physiological manner. In the last 10 years, five insulin analog preparations have become commercially available for clinical use in patients with type 1 diabetes mellitus: three 'rapid' or fast-acting analogs (insulin lispro, aspart, and glulisine) and two long-acting analogs (insulin glargine and detemir). This review highlights the specific pharmacokinetic properties of these new insulin analog preparations and focuses on their potential clinical advantages and disadvantages when used in children and adolescents with type 1 diabetes mellitus. The fast-acting analogs specifically facilitate more flexible insulin injection timing with regard to meals and activities, whereas the long-acting analogs have a more predictable profile of action and lack a peak effect. To date, clinical trials in children and adolescents have been few in number, but the evidence available from these and from other studies carried out in adults with type 1 diabetes suggest that they offer significant benefits in terms of reduced frequency of nocturnal hypoglycemia, better postprandial blood glucose control, and improved quality of life when compared with traditional insulins. In addition, insulin detemir therapy is unique in that patients may benefit from reduced risk of excessive weight, particularly during adolescence. Evidence for sustained long-term improvements in glycosylated hemoglobin, on the other hand, is modest. Furthermore, alterations to insulin/insulin-like growth factor I receptor binding characteristics have also raised theoretical concerns that insulin analogs may have an increased mitogenic potential and risk of tumor development, although evidence from both in vitro and in vivo animal studies do not support this assertion. Long-term surveillance has been recommended and further carefully designed prospective studies are needed to evaluate the overall benefits and clinical efficacy of insulin analog therapy in children and adolescents with type 1 diabetes.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Guided self-organization in a dynamic embodied system based on attractor selection mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. © 2014 by the authors; licensee MDPI, Basel, Switzerland.