Bibliographic record
Abstract
This paper will provide a discussion on virtual reality in sport. The terms virtual reality (VR), virtual environment (VE), and simulator, will be defined and discussed with regard to their potential as tools for the enhancement of elite athletic performance. Issues related to the need for these tools and the challenges of developing them, plus current examples of VR tools already in use by elite athletes, will be presented. VR refers to “an alternative world filled with 3-D computer generated images that respond to human movement” (Kaufman Broida & Germann, 1999, p. 95); this is similar to a virtual environment that is “made of three-dimensional [3-D] graphic images that are generated by computers for the expressed purpose of cognitive and physical interaction” (Caird, 1996, p. 124). According to Spring (1991), these are not new concepts, they are integral to work on scientific visualization, graphical user interfaces, hypertext, hypermedia, and visual languages. A truly virtual environment is one in which the user is a participant in an abstract space where physical machinery does not exist; only images and illusions are used to create a sense of reality. Helsel and Roth (1991) refer to the impact that resulted from the presentation of one of the first black and white movies, “The Arrival of the Train at the Station” (circa 1895), in which audience members physically jumped out of their seats to avoid being hit by the train. This is the kind of impact that researchers and developers would like to achieve in today's virtual environments, however audiences are now much more sophisticated and require environments that not only visually re-create reality, but also stimulate all the senses activated in the real environment. Simulators tend to be defined as either equipment or software that simulate all or part of the physical or cognitive elements of a specific act or skill. Caird (1996) argues that VE's are not very different from simulators that have essentially the same purpose. The difference is that VE's incorporate 3-D computer graphics with input devices that allow people to interact with the 3-D environment created around them whereas simulators do not. VR is immersive and takes simulators to another level. The types of input devices used to allow interaction are varied. For example, NASA uses devices such as eye phones, data gloves, and full body data suits to allow astronauts to explore various 3-D worlds (Spring, 1991). Paradiso and Hasan (2001) used compact, wireless, low pressure sensor cards inserted into a running shoe to monitor and transmit analog data, pressure measurements, acceleration data, angular and vertical spin coordinates, and orientation data which allow athletes to create interactive performances in simulated environments. As noted before, VE's need to stimulate as many as possible, if not all, of the senses stimulated by reality for the environment to be truly convincing and an effective training tool. This is one of the greatest challenges for virtual reality developers within the sports environments. The examples presented in this paper have been successful in stimulating a number of the senses as they are stimulated in reality. Wall, Bertrand, Gale, and Saunders (1998) asked sailors to sail a VR sailing simulator. The results found that performance in the simulator correlated highly with the real physical performance of the sailors (r = 0.99). Qualitative questionnaires given to the sailors also found that they perceived the correlation between the simulated and real physical performances to be high. However, they noted that the correlation between the results of the simulator performance and reality on the decision-making processes required during sailing was not as high. Another VR simulator, Computrainer created by RacermateTM is used by a number of elite cyclists, including athletes, at the Olympic Oval. The simulator applies pressure to the rear wheel of the rear wheel of the athletes' bike to simulate going uphill, on the flat, or downhill, and eases off when the athlete drafts behind another bike. The bike is also linked to a computer that provides the athlete with a 3-D visual picture of where they are and where their competitor is. This computerized aspect also provides a series of data on the athlete such as heart rate, rotations per minute, and calorie expenditure. A number of studies have found the simulator to be a very reliable and effective tool for simulating cycling (Carey & LaPort, 1999; Prasuhn, 2000). Preliminary work conducted at the Olympic Oval, University of Calgary, has been successful in creating a virtual oval that skaters can train the cognitive and visual components of speed skating at a variety of speeds over various distances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".