This paper proposes a novel loop-closure detection method based on localization using residual magnetism. Although the method uses only a magnetic sensor as external sensor, accurate loop-closure detection can be performed. This is because that precisely distinguishing of magnetic pattern is realized by using normalized cross-correlation. In addition, the method has usefulness that loop-closure detection is executed so fast. The effectiveness and usefulness of the method are shown through experiments.
This paper proposes an interactive training system for control of myoelectric prostheses. The proposed training system is capable of selecting suitable motions (EMG patterns) for each user by eliminating ineffective ones, and can also provide consistency between user's motor images and the corresponding prosthetic movements using a virtual prosthetic hand (VH). In the experiments performed, a one-day training session using the proposed system was conducted with nine healthy males (including an experienced) and an upper limb amputee. In addition, EMG discrimination ability of each subject during VH control without any feedback information was evaluated before and after the training to verify the training effects of the proposed system. The results showed that the discrimination rates for selected motions were sufficiently high (98.9 ± 1.24%) by using the proposed selection method, and the accuracy in discrimination for VH control was significantly improved after training (for healthy subjects and the amputee at the 0.1% and 1% level, respectively). It is therefore confirmed that the proposed system can be used for myoelectric prosthesis control training.
In this paper, we propose a novel method for frame-by-frame intermittent tracking to facilitate motion-blur-free video and resolve the tradeoff between brightness and motion blur when shooting moving objects by vision sensor. With the proposed method, vision-based tracking is performed when the shutter is open, and back-to-home control is performed when it is closed. This process occurs at dozens or hundreds of frames per second. We develop a prototype motion-blur-free microscope by implementing our frame-by-frame intermittent tracking method on a piezo-actuator-based microscopic tracking system, accelerated by a high-speed vision platform. Our motion-blur-free microscope can capture non-blurred 512 × 512 images at 125 fps with frame-by-frame intermittent tracking and its performance is verified by showing the experimental results from several moving scenes in microscopic view.