Summary
Harling and Edwards address the problem of segmenting gestures in a stream of data from a power glove. Their assumption is that when we purposefully want our hands to convey information, they will be tense. When the hand is "limp", the user is not trying to convey information.
Tension is measured by imagining rubber bands attached to the tip of the finger, one parallel to the x axis and the other to the y-axis. The rubber bands have certain elastic moduli, and the tension in the system can be solved with physics equations. To get an idea of overall hand tension, the values for each finger are summed.
They evaluate their idea by examining two different sayings in British sign language: "My name" and "My name me". They find dips in the 'tension graph' between each gesture, and claim an algorithm could segment at these points of low tension.
Discussion
Seems pretty nice. It's good to have an idea of what we can do to solve the segmentation issue. However, I wonder if some gestures are performed with a "limp" hand. Their idea of tension is maximized when the finger is either fully extended or fully closed, so anything where the finger is halfway will not work. Also, perhaps you naturally stand with your hand clenched in your relaxed position, so non-gestures would be tense.
I don't like that they sum the tension in each finger to get a total hand tension. I think we need information per finger, otherwise it seems like you could miss fingers moving in ways that kept the tension at the same level.
Their testing was /not/ very thorough. Another poor results section.
BibTeX
@inproceedings{harling1996handTensionSegmentation
,author = "Philip A. Harling and Alistair D. N. Edwards"
,title = "Hand Tension as a Gesture Segmentation Cue"
,booktitle = "Gesture Workshop"
,pages = "75-88"
,year = "1996"
}
1 comment:
Good point about the possibility of limp hand gestures. I was thinking about that recently, and thinking that a lot of 2 handed gestures may involve limp hands. It may still be useful to use limp hands as a segmentation/transfer point. We can just add an extra check and try to classify each segementation/transfer at the middle (timestamp-wise) to check if it is not actually a gesture on its own.
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