With the rapid development of the aging population, the health problems of the elderly have received more and more attention from all walks of life, and Alzheimer's disease has become an important disease that threatens the health of the elderly. Relevant studies have shown that mild cognitive impairment(MCI), as an early transitional stage of Alzheimer's disease, can be prevented or delayed by intervention. The pathophysiology of cognitive disorders is complex, and currently there is no specific targeted therapeutic drugs, and more and more clinical experimental data prove that modern rehabilitation methods such as cognitive training can improve cognitive disorders. Traditional cognitive training is mostly based on oral or pen and paper, and complete the training for patients through puzzle games, which consumes a lot of medical resources and is difficult to be standardized. In order to solve the disadvantages of traditional cognitive training, computer-assisted cognitive training has been widely used with the popularization of computers and the improvement of software technology. It takes the computer as the training platform, and uses the computer language to translate and program the methods and contents of cognitive training to train the patients. However, many computer-assisted cognitive training systems have unfriendly human-computer interaction, for they ignore the fact that MCI patients are mostly elderly people who have difficulty in using computers, which undoubtedly increase the burden on patients during training. Given the above problem, we design a computer-assisted cognitive training based on gesture recognition technology in this paper, which allows patients to achieve more natural human-computer interaction through gestures so that patients can focus more on training. The paper is organized as follows:

First, a recognition algorithm is proposed for the gestures of human-computer interaction in the training process in which we implement gesture segmentation based on skin color model, extract Fourier Descriptors of gesture contour as feature vectors and use SVM algorithm to train a classifier to recognize gestures. Then, the GUI of the system is designed to meet the task requirement of cognitive training for patients. When training starts, a number or a picture representing number is displayed randomly on the interface. After a while, the number or the picture disappears, and the system prompts the patient to make the corresponding gesture. The system predicts the number represented by patient's gesture according to the gesture recognition algorithm and determines whether the patient completes the training successfully. Finally, we do several tests and the results of tests show the accuracy of the algorithm and the feasibility of the GUI.

Key words: cognitive rehabilitation; gesture recognition; graphical user interface(GUI); convolutional neural network


Design of a Congniitive Rehabilitation System Based on Gesture Recognition

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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