Hand Pose Classification Based on Neural Networks
This is an incremental application of existing methods to a specific hand hygiene monitoring problem, with limited scope.
The researchers tackled hand pose classification using a pre-trained neural network on a limited dataset of 704 hand gesture images, achieving 100% accuracy in training for classifying presence of one hand, two hands, or no hand.
In this work, deep learning models are applied to a segment of a robust hand-washing dataset that has been created with the help of 30 volunteers. This work demonstrates the classification of presence of one hand, two hands and no hand in the scene based on transfer learning. The pre-trained model; simplest NN from Keras library is utilized to train the network with 704 images of hand gestures and the predictions are carried out for the input image. Due to the controlled and restricted dataset, 100% accuracy is achieved during the training with correct predictions for the input image. Complete handwashing dataset with dense models such as AlexNet for video classification for hand hygiene stages will be used in the future work.