  IMUs are gaining significant importance in the field of hand gesture
analysis, trajectory detection and kinematic functional study. An Inertial
Measurement Unit (IMU) consists of tri-axial accelerometers and gyroscopes
which can together be used for formation analysis. The paper presents a novel
classification approach using a Deep Neural Network (DNN) for classifying hand
gestures obtained from wearable IMU sensors. An optimization objective is set
for the classifier in order to reduce correlation between the activities and
fit the signal-set with best performance parameters. Training of the network is
carried out by feed-forward computation of the input features followed by the
back-propagation of errors. The predicted outputs are analyzed in the form of
classification accuracies which are then compared to the conventional
classification schemes of SVM and kNN. A 3-5% improvement in accuracies is
observed in the case of DNN classification. Results are presented for the
recorded accelerometer and gyroscope signals and the considered classification
schemes.
