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Sign Language is used by the deaf community all over world. The workpresented here proposes a novel one-dimensional deep capsule network (CapsNet)architecture for continuous Indian Sign Language recognition by means ofsignals obtained from a custom designed wearable IMU system. The performance ofthe proposed CapsNet architecture is assessed by altering dynamic routingbetween capsule layers. The proposed CapsNet yields improved accuracy values of94% for 3 routings and 92.50% for 5 routings in comparison with theconvolutional neural network (CNN) that yields an accuracy of 87.99%. Improvedlearning of the proposed architecture is also validated by spatial activationsdepicting excited units at the predictive layer. Finally, a novelnon-cooperative pick-and-predict competition is designed between CapsNet andCNN. Higher value of Nash equilibrium for CapsNet as compared to CNN indicatesthe suitability of the proposed approach.

Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory