Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs
This work addresses sign language translation for improved accessibility, but it is incremental as it builds on existing CNN methods with a context-specific array.
The authors tackled sign language recognition by proposing a one-dimensional CNN array architecture that classifies gestures from Indian sign language using a wearable IMU device, achieving peak accuracies of 94.20% for general sentences and 95.00% for interrogative sentences compared to 93.50% with a conventional CNN.
Advancements in gesture recognition algorithms have led to a significant growth in sign language translation. By making use of efficient intelligent models, signs can be recognized with precision. The proposed work presents a novel one-dimensional Convolutional Neural Network (CNN) array architecture for recognition of signs from the Indian sign language using signals recorded from a custom designed wearable IMU device. The IMU device makes use of tri-axial accelerometer and gyroscope. The signals recorded using the IMU device are segregated on the basis of their context, such as whether they correspond to signing for a general sentence or an interrogative sentence. The array comprises of two individual CNNs, one classifying the general sentences and the other classifying the interrogative sentence. Performances of individual CNNs in the array architecture are compared to that of a conventional CNN classifying the unsegregated dataset. Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved with the proposed CNN array in comparison to 93.50% for conventional CNN assert the suitability of the proposed approach.