Nowadays, many interface devices or training systems have been developed with recent developments in IT technology, but only a few training systems for developmentally disabled people have been introduced. In this paper, we present a real-time, interactional and situational training system based on augmented reality in order to improve cognitive capability and adaptive ability in the daily lives of developmentally disabled people. Our system is specifically based on serving food in restaurants. It allows disabled people wearing the HMD attached with camera to conduct the training to cope with a series of situations safely while serving customers food and drinks and take the training session as much as they want. After experimenting on our presented system for 3 months, we found that they actively participated in the training and their cognitive abilities increasingly went faster through repeated training, resulting in the improvement in their cognitive ability and their ability to deal with situations.

In this paper, we present a real-time hand pose recognition method to provide an intuitive user interface through hand poses or gestures without a keyboard and a mouse. For this, the areas of right and left hands are segmented from the depth camera image, and noise compensation is performed. Then, the rotation angle and the centroid point of each hand area are calculated. Subsequently, joint points and end points of a finger are detected by expanding a circle at regular intervals from a centroid point of the hand. Lastly, the hand pose is recognized by matching between the current hand information and the hand model of previous frame and the hand model is updated for the next frame. This method enables users to predict the hidden fingers through the hand model information of the previous frame using temporal coherence in consecutive frames. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 95% and the performance indicated over 32fps. The proposed method can be used as a contactless input interface in presentation, advertisement, education, and game applications.

In this paper, a motion evaluation technique for rehabilitation training is introduced. Motion recognition technologies have been developed for determining matching motions in the training set. However, we need to measure how well and how much of the motion has been followed for training motion evaluation. We employed a Finite State Machine as a framework of motion evaluation. For similarity analysis, we used weighted angular value differences although any template matching algorithm may be used. For robustness under illumination changes, IR LED's and cameras with IR-pass filter were used. Developed technique was successfully used for rehabilitation training of the disabled. Therapists appraised the system as practically useful.