Younggeun Choi

Early childhood education by hand gesture recognition using a smartphone based robot

We propose a light and fast hand gesture recognition method using geometric feature for a smartphone based robot and apply it to early childhood mathematics education. The feature of hand gesture is defined by the number of extrema in the plot for the distances between the center point of hand and the outer points of hand from active contour model or snakes. The region of interest (ROI) is continuously updated by Continuously Adaptive Mean Shift Algorithm (CamShift) algorithm, and the snake model is used to make the outer points sequential efficiently. A mathematics learning application for an Android OS smartphone based robot is developed using the hand gesture recognition algorithm. The experiment with Korean children (5-6 years of age) is conducted to evaluate if hand gesture based HRI could promote their mathematics learning. The result suggests that the idea of hand gesture based HRI for early childhood education is feasible and that children can learn mathematics by hand gesture based interaction with a robot.

Evolutionary algorithm for a genetic robot's personality based on the Myers-Briggs Type Indicator

The genetic robot has many configurable genes that contribute to defining the robots personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robots personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes MBTI-EAGRP. It is a fully autonomic gene-generative algorithm for a genetic robots personality in a mobile phone. After grasping the user preferences through MBTI assessment using the neural network algorithm, the evolutionary algorithm generates and evolves a gene pool that customizes the robots genome so that it closely matches a simplified set of personality features preferred by the user. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios and real MBTI measurements.

RAPAEL: Wearable Technology and Serious Game for Rehabilitation

In this demonstration paper, we introduce our RAPAEL rehabilitation system. It is designed for treatment of those who are suffering from brain diseases or disorders. The purpose of our system is to turn boring and unpleasant conventional rehabilitation exercises into more exciting and inspiring one. We developed an IoT-based wearable device and serious game for medical use.

Erratum to: Development of a low-cost wearable sensing glove with multiple inertial sensors and a light and fast orientation estimation algorithm

Correct capturing the movement of hands and fingers provides natural ways of interacting with computers. However, developing a glove-based device for such interaction has been very expensive and there were technical problems such as a complicate motion measurement algorithm in limited embedded resources and a complicate calibration process. We present a practical development of a low-cost and lightweight wearable sensing glove using only one CPU and seventeen IMUs. It transmits the captured movement data of seventeen joints of hand and wrist to a host machine via Bluetooth communication. We also propose a light and fast orientation estimation algorithm for the glove system, which should compute orientations and calibrations for seventeen inertia measurement units (IMUs) in real time. The seventeen individual IMUs are composed of an accelerometer, a gyroscope and a magnetometer based on micro electro-mechanical system technology. The magnetometer has sensor bias and scale factor errors, which vary with temperatures and places. Moreover, as the wearable sensing glove has a limited battery life and a cheap embedded processor, it can only utilize limited memory and computation power. Therefore, the algorithm should compute the attitude of the IMUs and calibrate the magnetic sensor in real time with very low computational load, by maintaining only a valid subset of data points. Our experimental results indicate that the algorithm achieves sufficient levels of real-time computation and accuracy.