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Objective To determine the clinical effectiveness of adding virtual reality via the Nintendo Wii console and its Wii Balance Board to physiotherapy treatment in patients with total hip arthroplasty. Design Randomized controlled trial. Setting Clinical Hospital San Borja Arriaran, Santiago, Chile. Participants A total of 73 patients over 50 years of age with total hip arthroplasty were randomly allocated to two groups. Interventions The control group (n = 37) received 6 weeks of physiotherapy treatment; the intervention group (n = 36) received the same treatment plus virtual reality exercises with the Nintendo Wii console. Outcome measures The two groups were assessed at baseline and after the 6 weeks of treatment. The primary outcome assessed was the function with the WOMAC questionnaire. The secondary outcomes were the Berg Balance Scale, distance covered with the six-minute walk test, and difference in weight load on the lower extremities. Results A total of 73 patients, 37 patients in the control group (20 women; mean age of 70.9 ± 9.16 years) and 36 patients in the intervention group (18 women; mean age of 70.39 ± 9.02 years) were analyzed. At the end of the treatment, the difference between groups for the total WOMAC score was −10.4 points ( p = 0.00), 4.7 points ( p = 0.00) for the Berg Balance Scale, and 45.2 mt ( p = 0.00) for the six-minute walk test All differences were in favor of the intervention group. Conclusions In the short term, the addition of virtual reality via the Nintendo Wii and its Wii Balance Board platform showed statistically significant differences in the function of patients with total hip replacement, but these differences were not minimally clinically important. Trial registration: This research was registered in the Clinical Trials Registry of Australia and New Zealand, with reference ACTRN12618001252202.

In this paper a study is conducted in order to evaluate three different strategies of haptic feedback for texture discrimination in virtual environments. Specifically, both force and vibrotactile feedback have been evaluated, as well as the direct use of the sense of touch, to detect different textures. To this end, a force feedback Phantom device, a custom built vibrotactile dataglove and paper palpable prototypes, which represent an ideal model of tactile feedback, have been compared. These three methods have been used to detect two types of patterns, one formed by different geometrical shapes, and the other with different grooves width. Results show that the vibrotactile dataglove has a notable behaviour in the detection of textures where the frequency of tactile stimuli varies, and it is even useful to detect more complex textures.

In the research community, Collaborative Virtual Environments (CVEs) developers usually refer to the terms awareness and feedback as something necessary to maintain a fluent collaboration when highly interactive task have to be performed. However, it is remarkable that few studies address the effect that including special kind of awareness has on the task performance and the user experience.This paper proposes how to face the implementation of awareness in order to be taken into account early in the development of a CVE. In addition, it is also described an experiment that was carried out to evaluate the effect of providing some visual cues, showing that users tend to make more mistakes when they are not provided.

The development of 3D user interfaces is mostly focused on technology and the ways of using it, and so the main concerns are the selection of hardware, software and interaction techniques. The process of development itself is as important as these issues, but it is usually ignored or poorly documented. This paper introduces the TRES-D methodology, and illustrates its application in the development of three different glove-based interfaces, not only to show the benefits of using these devices, but also the benefits of using such a methodological framework.

A stand-alone machine learned turbulence model is applied for the solution of integral boundary layer equations, and issues and constraints associated with the model are discussed. The results demonstrate that grouping flow variables into a problem relevant parameter for input during machine learning is desirable to improve accuracy of the model. Further, the accuracy of the model can be improved significantly by incorporation of physics-based constraints during training. Data driven machine learning training requires trial-and-error approach, shows oscillations in a posteriori predictions, and shows unphysical results when used with arbitrary initial condition, as the query is essentially extrapolations. Physics informed machine learning addresses the above limitations, and is identified to be a viable approach for development of machine learned turbulence model.