Takashi Ozeki

Remote Touch Pointing for Smart TV Interaction

Increasing in the size of TV, Smart TV in connected to internet is spreading. Remote-control of TV is complicate by increase of contents. In this research, we propose a Remote Touch TV that is able to operate at a large screen intuitively. Remote Touch TV is a new remote-control method for smart TV using Remote Touch Pointing. Remote Touch Pointing set on the base point and the operating point as part of the body. It points out objective area using the extended line of them. It is start up by a user stands in front of TV. And it power down by moving away from the TV. Menu for selecting the contents is displayed by raising users hand. The user is able to select an objective content by Pointing and Tap. The operation procedures in remote control and in the Remote Touch TV were compared using the sequence diagrams. As a result, we clarified that the actor element and recognition target are less than the remote-control in the proposal method. Also we clarified to reduce the operational procedure by proposal method.

Extraction of relations between behaviors by lecturer and students in lectures

In this paper, we discuss the extraction of relations between lecturer and students in lectures by using multi-layered neural networks. Here, the relations among a few features concerning on the behaviors of the lecturer and students can be represented by multi-layered neural networks with the time-delay. Furthermore, we introduce a structural learning algorithm with forgetting for neural networks for the extraction of rules in the interaction. The above time-series models are analyzed focusing on weights in multi-layered neural networks. Concretely, we analyze which lecturers behaviors (face and hand movements and loudness of speech) give great influences on behaviors by students (face movements). On the contrary, we analyze which students behavior (face movement) give great influences on behaviors by lecturer.

Analysis of Non-verbal Behaviors by Students in Cooperative Learning

In this paper, we discuss the relationship between non-verbal behaviors and understandings by students in the cooperative learning. First, we detect non-verbal behaviors by students by using image processing methods. Next, we propose a modeling method for non-verbal behaviors. Furthermore, we discuss the relationship between non-verbal behaviors and understandings by students based on the above models.

Paper analysis of reading and writing behaviors for digital contents

In this paper, we discuss the change of page number and image processing methods for discrimination between behaviors in reading contents and taking notes for contents on the display. First, scene images for contents can be recorded by a sunglasses-type camera. Next, a few features can be extracted based on color information from these images and the discrimination procedure can be executed by the discrimination analysis and the classification method by using multi-layered neural networks. Finally, we evaluate the discrimination precision and discuss the relations among page transition, behaviors by students and their notes. This electronic document is a “live” template. The various components of your paper (title, text, heads, etc.) are already defined on the style sheet, as illustrated by the portions given in this document.

Analysis of behaviors by lecturer and students in lectures based on piecewise auto-regressive modeling

In this paper, we discuss the analysis of behaviors by lecturer and students in lectures. In lectures, the relationships between lecturer and students depend on not only contents but also the loudness of speech and gestures by lecturer. The behaviors consisting of head and eye movements by students can often represent how they have the interest with the lecture. First, we extract the loudness of speech and the face direction as behaviors by lecturer and the face direction as behaviors by students. Next, we construct piecewise auto-regressive (AR) models for their behaviors. Here, the piecewise AR models can be constructed based on the residual error. Finally, we show the analysis results for a real lecture based on the piecewise AR modeling.

Analysis of Behaviors of Participants in Meetings

In this paper, we analyze the behaviors of participants in two types of meetings (brainstorming and decision-making). First, we introduce the use of participant behavior based on facial movement. Next, we propose a method for modeling the behaviors of participants based on multi-layered neural networks. Lastly, based on our experimental results, we discuss the relationships between the meeting phase, participant behaviors, and the model parameters in these two types of meetings. Our results show the parameters in the above models to be strongly related to the behaviors and ideas of the participants in these two types of meetings.

Relationships between behaviors by applicants and interviewer in interview

In interviews, the evaluation by the interviewer is affected by not only the reply but also behaviors (eye and face movements) by applicants. Moreover, the evaluation by the interviewer is communicated with applicant as the behavior by the interviewer. Therefore, the interaction between the behaviors by the interviewer and the applicant occurs. In this paper, we show that the evaluation by the interviewer is strongly influenced by behaviors by applicants and the relationship between the evaluation by the interviewer and the behaviors by applicants.

Analysis of Behaviors by Audience in Lectures by Using Time-series Models

In this paper, the dominant behaviors define by the face direction of the speaker and audience in lectures are analyzed by using the time-series models. First, we detect the number of skin-colored pixels in face region of speaker and audience as features for behaviors by them. Next, we construct piecewise time series models for these features for the speaker and audience. Finally, we analyze the synchronization phenomena in speaker and audience by comparing time series models. Moreover, we discuss the relationships among notes, test and behaviors by audience.

Extraction of relationships between page transition and understanding for contents

In this paper, we consider a learning environment, which students solve the problem while reading digital contents. Here, we extract the relationship between the frequency for reading each page and the understanding for given problems. The above relationship can be modeled by using neural networks. Furthermore, we use a learning method with forgetting for neural networks for the purpose of clarification of internal representations. Finally, we have analyzed the relationship between the frequency for each page and the understanding for given contents based on the internal representations of multi-layered neural networks.