In Intelligent User Interface (IUI) group, we research on user interface where human-data interaction occurs is conducted. Various devices ranging from smartphones and tablets to wearable devices and VR are used in the lab. Also, research on analysis of sensor information such as touch information and hand-written data, and VR technology is done for application to education as well.
Analysis of Handwritten Stroke Information in Japanese Long-passage Reading Problem
In this research, we estimate whether learners answered problems by intuition or not by using Japanese long-passage reading problem.
In recent years, it has become easier to use touch information and handwritten information as learning log data of learners. Information such as pen pressure and time interval will be a clue to understanding the mental condition of the learner who is answering. Therefore, in this research, we aim to judge the thought of the learner by analyzing the handwritten data of the long-term reading comprehension problem.
Classification of Solutions using Hand Written Data
This research is a study on analyzing handwritten answer data of geometric figure problems and to estimate the learner’s level of understanding by classifying their solution method.
By analyzing the handwritten data during learning, it is possible to provide optimum learning support for each learner in on-line learning where it is difficult to monitor the behavior of the learner.
Stroke-based authentication on smartphone
Existing smartphone authentications such as patterns, fingerprints, and face ID contain some risks to be cracked by others. To overcome these risks, continuous authentication by touch strokes can be effective. In this study, we aim to improve authentication accuracy and robustness against others in a stroke-based authentication. And we also try to build an authentication system with high imitation-resistance, and to run it on cloud servers.
Active Authentication on Smartphone
Active authentication which uses individual behavioral features is one of the new authentication systems in smartphones. Smartphones are equipped with several authentication functions, but the damage that they are cracked is increasing. By using active authentication, we can realize “low reproducibility” and “rapid detection of illegal users”.
In our laboratory, we are conducting active authentication research using touch information obtained from the touch panel and pressure sensor built in the smartphone.
Authentication combining LeapMotion and Numeric Keypad
This research is a study on two-factor authentication combining password authentication and biometric authentication. The purpose of this research is to enhance security in authentication. Biometric authentication is an authentication method that uses biometric information (personal characteristics) such as fingerprint authentication and face authentication. Biometric authentication requires less time and effort than password authentication, but the risk of theft of biometric information is increasing with the development of technology. If once biometric information is stolen, it is difficult to reuse as an authentication key because biometric information cannot be changed like password. To solve the aforementioned problems, we try to combine changeable password authentication and biometric authentication using behavioral features such as hand movement and shape at the time of input. Specifically, we assume the environments where a user inputs a numeric password into a system such as an ATM and door locker keypad.