This paper presents a motor learning method based on the presenting of a personalized target motion, which we call a tentative goal. While many prior studies have focused on helping users correct their motor skill motions, most of them present the reference motion to users regardless of whether the motion is attainable or not. This makes it difficult for users to appropriately modify their motion to the reference motion when the difference between their motion and the reference motion is too significant. This study aims to provide a tentative goal that maximizes performance within a certain amount of motion change. To achieve this, predicting the performance of any motion is necessary. However, it is challenging to estimate the performance of a tentative goal by building a general model because of the large variety of human motion. Therefore, we built an individual model that predicts performance from a small training dataset and implemented it using our proposed data augmentation method. Experiments with basketball free-throw data demonstrate the effectiveness of the proposed method.

We propose an action quality assessment (AQA) method that can specifically assess target action quality with ignoring scene context, which is a feature unrelated to the target action. Existing AQA methods have tried to extract spatiotemporal features related to the target action by applying 3D convolution to the video. However, since their models are not explicitly designed to extract the features of the target action, they mis-extract scene context and thus cannot assess the target action quality correctly. To overcome this problem, we impose two losses to an existing AQA model: scene adversarial loss and our newly proposed human-masked regression loss. The scene adversarial loss encourages the model to ignore scene context by adversarial training. Our human-masked regression loss does so by making the correlation between score outputs by an AQA model and human referees undefinable when the target action is not visible. These two losses lead the model to specifically assess the target action quality with ignoring scene context. We evaluated our method on a diving dataset commonly used for AQA and found that it outperformed current state-of-the-art methods. This result shows that our method is effective in ignoring scene context while assessing the target action quality.

This scheme describes a procedure that uses the motion characteristics of the videos to calculate the embedding tolerance for imperceptible video watermarking in wavelet domain. This procedure exploits the perceptual properties of HVS and motion content of video sequence adoptively calculating the embedding tolerance of watermark in order to improve the imperceptibility of watermarking according to the content of video. The experiment results indicate that this method can effectively reduce the degradation in the video quality.

Digital watermarking technology can be applied to reference services that provide digital information related to pictures taken by mobile phone cameras. Conventional reference services for mobile phones are designed for still images and detect watermarks from single pictures. We propose a video watermarking scheme for fast, robust detection to develop a reference service for video. To handle spatial synchronization, the scheme uses the rectangular tracking technique, and to provide temporal synchronization and robustness against noise, it uses the watermark technique based on the SFPSS method. We also introduce a quantitative evaluation method to determine reliability of the detection results. Finally, we evaluate the scheme's implementation on a mobile phone and demonstrate its efficiency.

An open distributed control system (DCS), which consists of a large number of devices and a single open network interconnecting those devices, is now used in many automation areas. One critical problem of the DCS, however, is that because there is massive traffic on the network, the system integrator must carefully tune the network traffic after the construction of the system to assure its control performance. Hence, a DCS simulator is strongly needed. In our study, we propose an object-oriented design pattern approach as a uniform, rapid and accurate method of modeling and simulating a DCS. Two special design patterns are proposed: the Statechart pattern which defines classes and the state-transition execution mechanism for realizing the dynamic behavior of devices; and the device-constructor pattern which defines classes and their instantiation mechanism for realizing the structure of devices composed of many kinds of sensors and actuators. The systematic procedures from those patterns to the executable Java code of the simulation model are also discussed. Furthermore, the effectiveness of our approach was investigated through actually developing an in-house Java-based DCS simulator.