Assistive Hand Gesture Glove for Hearing and Speech Impaired
Abhi Zanzarukiya, Bhargav Jethwa, Milit Panchasara, Rutu Parekh · 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) · 2020
Hand based gestures are words of communication for people impaired of speech and hearing. This results in communication mismatch between such impaired people and a normal one. A deaf and dumb person many times finds difficulty in informing about basic phrases or words representing certain actions like “How are you?”, “Yes”, etc. to a normal human. To tackle this issue, one hand gesture recognition system is presented which uses a sensor and a microcontroller to capture a gesture movement in the form of a signal. This hand gesture recognition system uses 1D Convolutional Neural Network (1D-CNN) which can extract feature directly from the raw temporal signals captured. The resulted word or phrase is communicated to a normal person through the Mobile phone of a disabled person in terms of audio voice and text-based notification. Furthermore, to reduce the latency in predicting output, the trained 1D-CNN model is deployed in Android phone itself rather than running the model on the server. The trained model achieves recognition accuracy of 97.96% on test data consisting of many samples of 10 different patterns.