A sliding window approach to natural hand gesture recognition using a custom data glove

This paper explores the recognition of hand gestures based on a data glove equipped with motion, bending and pressure sensors. We selected 31 natural and interaction-oriented hand gestures that can be adopted for general-purpose control of and communication with computing systems. The data glove is custom-built, and contains 13 bend sensors, 7 motion sensors, 5 pressure sensors and a magnetometer. We present the data collection experiment, as well as the design, selection and evaluation of a classification algorithm. As we use a sliding window approach to data processing, our algorithm is suitable for stream data processing. Algorithm selection and feature engineering resulted in a combination of linear discriminant analysis and logistic regression with which we achieve an accuracy of over 98.5% on a continuous data stream scenario. When removing the computationally expensive FFT-based features, we still achieve an accuracy of 98.2%.