  Sign Language is used by the deaf community all over world. The work
presented here proposes a novel one-dimensional deep capsule network (CapsNet)
architecture for continuous Indian Sign Language recognition by means of
signals obtained from a custom designed wearable IMU system. The performance of
the proposed CapsNet architecture is assessed by altering dynamic routing
between capsule layers. The proposed CapsNet yields improved accuracy values of
94% for 3 routings and 92.50% for 5 routings in comparison with the
convolutional neural network (CNN) that yields an accuracy of 87.99%. Improved
learning of the proposed architecture is also validated by spatial activations
depicting excited units at the predictive layer. Finally, a novel
non-cooperative pick-and-predict competition is designed between CapsNet and
CNN. Higher value of Nash equilibrium for CapsNet as compared to CNN indicates
the suitability of the proposed approach.
