Publikationen

This paper explores the recognition of hand gestures based on a dataglove equipped with motion, bending and pressure sensors. We se-lected 31 natural and interaction-oriented hand gestures that canbe adopted for general-purpose control of and communication withcomputing systems. The data glove is custom-built, and contains13 bend sensors, 7 motion sensors, 5 pressure sensors and a magne-tometer. We present the data collection experiment, as well as thedesign, selection and evaluation of a classification algorithm. As weuse a sliding window approach to data processing, our algorithm issuitable for stream data processing. Algorithm selection and featureengineering resulted in a combination of linear discriminant anal-ysis and logistic regression with which we achieve an accuracy ofover 98. 5% on a continuous data stream scenario. When removingthe computationally expensive FFT-based features, we still achievean accuracy of 98. 2%.