Summary
Komura and Lam present a method for mapping the movements of fingers (while wearing a data glove) and the hand into controlling a character in a 3D game, such as a character walking, running, hopping, and turning. They achieve this in two steps. The first is to give the user an on-screen example of a character walking, and then have the user mimic the action. During this stage, the data from the glove is calibrated to determine the periodicity of movements, etc, and a mapping from the finger movements to the movements of the character of the screen is made. The movement of each finger is compared to the movement of each end-point of the figure (legs, chest, etc), and the finger feature-vector that has the smallest angle to a given body part's feature vector (velocities and directions) is mapped to that body part. The mapping of function values (movement in finger to amount of movement in body) is simply made as the regression of a B-spline. The user can then move his hands around and make the character walk/run/jump with the mapped values. They perform a user study by making people run a character through a narrow set of passages, and find that users run through just as fast using keyboard vs. the glove, but tend to be more accurate and have fewer collisions with walls using the glove. They attribute this to the intuitive interface of using one's hands to control a figure.
Discussion
The one thing I did like about this paper was the way they mapped fingers to a set of pre-defined motions and then mapped the movements of the fingers to those of the characters. It seemed neat, but I don't think it has any research merit.
Why do you even have to learn anything? Everything they do is so rigid and pre-defined anyway, like the mapping of 2/4 legs of a dog to each finger with a set way for computing the period delay between front/rear legs. Why not just force a certain leg on the character to be a certain finger and avoid mapping altogether? Maybe you could still fit the B-spline to get a better idea of sensitivity, but the whole cosine thing is completely unnecessary.
They only use one test and it's very limited, so I don't think they can make the claim that "is is possible to conclude that the sensing glove controlling more effective when controlling the character more precisely." I also want standard deviation and significance levels for Tables 1 and 2, though for such a small sample size these might not be meaningful.
BibTeX
@article{1182569,
author = {Taku Komura and Wai-Chun Lam},
title = {Real-time locomotion control by sensing gloves: Research Articles},
journal = {Comput. Animat. Virtual Worlds},
volume = {17},
number = {5},
year = {2006},
issn = {1546-4261},
pages = {513--525},
doi = {http://dx.doi.org/10.1002/cav.v17:5},
publisher = {John Wiley and Sons Ltd.},
address = {Chichester, UK, UK},
}
2 comments:
The learning aspect of this paper was completely unnecessary, since they simply have to synchronize the legs with the phase of the fingers, which shouldn't be difficult.
The focus should have been much more on the user's reaction to the gloves instead of how to align the phases of the legs.
I think this paper tried to find research merit in something that didn't really warrant it. I didn't like the idea in the first place, primarily because if one maps locomotion to the fingers, should one map hand motion to the feet? It's an extreme analogy perhaps, but I just think Iba's paper had a better idea for something similar.
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