As a result of the growth of sensor-enabled mobile devices, in recent years, users can utilize diverse digital contents everywhere and anytime. However, the interfaces of mobile applications are often unnatural due to limited computational capability, miniaturized input/output controls, and so on. To complement the poor user interface (UI) and fully utilize mobility as feature of mobile devices, we explore possibilities for a new UI of mobile devices. This paper describes the method for recognizing and analyzing a user's continuous action including the user's various gestures and postures. The application example we created is mobile game called AM-Fishing game on mobile devices that employ the accelerometer as the main interaction modality. The demonstration shows the evaluation for the system usability.

The hand posture estimation system by searching a similar image from a vast database, such as our previous research, may cause the increase of processing time, and prevent realtime controlling of a robot. In this study, the authors proposed a new estimation method of human hand posture by rearranging a large-scale database with the Self-Organizing Map including self-reproduction and self-annihilation, which enables two-step searches of similar image with short period of processing time, within small errors, and without deviation of search time. The experimental results showed that our system exhibited good performance with high accuracy within processing time above 50 fps for each image input with a 2.8 GHz CPU PC.

The authors propose a system for searching the shape of human hands and fingers in real time and with high accuracy, without using any special peripheral equipment such as range sensor, PC cluster, etc., by a method of retrieving similar image quickly with high accuracy from a large volume of image database containing the complicated shapes and self-occlusions. In designing the system, we constructed a database in a way to be adaptable even to differences among individuals, and searched CG images of hand similar to unknown hand image, through extraction of characteristics using high-order local autocorrelational patterns, reduction of the amount of characteristics centering on principal component analysis, and prior rearrangement of data corresponding to the amount of characteristics. As a result of experiments, our system performed high-accuracy estimation of human hand shape where mean error was 7 degrees in finger joint angles, with the processing speed of 30 fps or over.

This paper proposes the design of a physically accurate spine model and its application to estimate three dimensional spine posture from the frontal and lateral views of a human body taken by two conventional video cameras. The accurate spine model proposed here is composed of rigid body parts approximating vertebral bodies and elastic body parts representing intervertebral disks. In the estimation process, we obtain neck and waist positions by fitting the Connected Vertebra Spheres Model to frontal and lateral silhouette images. Then the virtual forces acting on the top and the bottom vertebrae of the accurate spine model are computed based on the obtained neck and waist positions. The accurate model is deformed by the virtual forces, the gravitational force, and the forces of repulsion. The model thus deformed is regarded as the current posture. According to the preliminary experiments based on one real MR image data set of only one subject person, we confirmed that our proposed deformation method estimates the positions of the vertebrae within positional shifts of 3.2 6.8 mm. 3D posture of the spine could be estimated reasonably by applying the estimation method to actual human images taken by video cameras.