  A music glove instrument equipped with force sensitive, flex and IMU sensors
is trained on an electric piano to learn note sequences based on a time series
of sensor inputs. Once trained, the glove is used on any surface to generate
the sequence of notes most closely related to the hand motion. The data is
collected manually by a performer wearing the glove and playing on an electric
keyboard. The feature space is designed to account for the key hand motion,
such as the thumb-under movement. Logistic regression along with bayesian
belief networks are used learn the transition probabilities from one note to
another. This work demonstrates a data-driven approach for digital musical
instruments in general.
