A multi-class pattern recognition system for practical finger spelling translation

Summary Hernandez et al create a simple, cheap, accelerometer-based glove to track hand postures and classify gestures using dimensionality reduction and a decision tree. The glove consists of five accelerometers attached to the fingers between the second and third joints. By relating the accelerometers to the pull of gravity the overall position of a finger (or at least that of the segment of the finger). By summing the x-components of each accelerometer and the y-components, they form an global x and y position. The y-position of the index finger is taken as a measure of the

Discussion The amount of data reduction would seem to oversimplify hand posture at least in general. I'm still not convinced that it's adequate to describe the position of the fingers simply by an average or overall curvature and spread when two distinct gestures may differ only in the bend of a single joint. While this hand measurement system seems to work well for signing, it doesn't seem to be useful for general gesturing, since you could in theory have two different gestures with the fingers in the same orientation but different hand positioning. Also, accelerometers tend to be very sensitive to noise, making dynamic, moving gestures difficult.

Reference Hernandez-Rebollar, J. L., R. W. Lindeman, et al. (2002). A multi-class pattern recognition system for practical finger spelling translation. Multimodal Interfaces, 2002. Proceedings. Fourth IEEE International Conference on.