Brainwaves (EEG signals) and mouse behavior information are shown to be useful in predicting academic emotions, such as confidence, excitement, frustration and interest. Twenty five college students were asked to use the Aplusix math learning software while their brainwaves signals and mouse behavior (number of clicks, duration of each click, distance traveled by the mouse) were automatically being captured. It is shown that by combining the extracted features from EEG signals with data representing mouse click behavior, the accuracy in predicting academic emotions substantially increases compared to using only features extracted from EEG signals or just mouse behavior alone. Furthermore, experiments were conducted to assess the prediction accuracy of the system at points during the learning session where several of the extracted features significantly deviate in value from their mean. The experiments confirm that the prediction performance increases as the number of feature values that deviate significantly from the mean increases.
Tele-medicine systems run the risk of unauthorized access to medical records, and there is greater possibility for the unlawful sharing of sensitive patient information, including children, and possibly showing their private parts. Aside from violating their right to privacy, such practices discourage patients from subjecting themselves to tele-medicine. The authors thus present an automatic identity concealment system for pictures, the way it is designed in the GetBetter tele-medicine system developed under a WHO/TDR grant. Based on open-source face- and eye-detection algorithms, identity concealment is executed by blurring the eye region of a detected face using pixel shuffling. This method is shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image. This is useful when subsequent image processing techniques are employed, such as when identifying the type of lesions based on images of the skin.
Tele-medicine systems run the risk of unauthorized access to medical records, and there is greater possibility for the unlawful sharing of sensitive patient information, including children, and possibly showing their private parts. Aside from violating their right to privacy, such practices discourage patients from subjecting themselves to tele-medicine. The authors thus present an automatic identity concealment system for pictures, the way it is designed in the GetBetter tele-medicine system developed under a WHO/TDR grant. Based on open-source face- and eye-detection algorithms, identity concealment is executed by blurring the eye region of a detected face using pixel shuffling. This method is shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image. This is useful when subsequent image processing techniques are employed, such as when identifying the type of lesions based on images of the skin.
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