Abnormal head movement classification using deep neural network DNN

Abnormal head movements play a crucial role in diagnoisis of varity diseases. Moreover, different studies considered with these type of information. In addition, the gestures based mainly on head movement which can be employed in many applications such as using head-nodding or shaking to feedback content-related feedback, detect and interpret the emotion, gaze orientation, focus of attention, driver assistance system and so on. In this paper, a new method proposed to detect and classify the flopping head movements as normal or abnormal based on Convolution Neural Network CNN in the term of special sense interaction and behavioral studies with this movement. The proposed system based on deep learning employing the Convolution Neural Network CNN as most promising approach to deal with lighting condition (illumination change) and distortion and noise. Normal Abnormal Head Movement Dataset (NAHM) static images dataset gathered and used in proposed system implementation. This dataset provided the various image conditions and subjects to prevent the overfitting and under fitting problem that appears with publically datasets. The proposed frame work presents robust learning ability based on accuracy and lose functions which achieved training loss: 0.0106, training accuracy: 0.9980, validation loss: 0.0968 and validation accuracy: 0.9831.