Sung-jung Cho

Magic wand: a hand-drawn gesture input device in 3-D space with inertial sensors

This paper presents a gesture input device, magic wand, with which a user can input gestures in 3-D space, inertial sensors embedded in it generate acceleration and angular velocity signals according to a users hand movement. A trajectory estimation algorithm is employed to convert them into a trajectory on 2-D plane. The recognition algorithm based on Bayesian networks finds the gesture model with the maximum likelihood from it. The recognition performance of the proposed system is quite promising; the writer-independent recognition rate was 99.2% on average for the database of 15 writers and 13 gesture classes.

Inertial sensor based recognition of 3-D character gestures with an ensemble classifiers

We present a 3-D input medium based on inertial sensors for on-line character recognition and an ensemble classification scheme for the recognition task. The system allows user to write a character in the air as a gesture, with a sensor-embedded device held in hand. The kinds of sensors used are 3-axis accelerometer and 3-axis gyroscope generating acceleration and angular velocity signals respectively. For character recognition, we used the technique of FDA (Fisher discriminant analysis). We tried different combinations of sensor signals to test the recognition performance. It is also possible to estimate a 2-D handwriting trajectory from the sensor signals. The best recognition rate of 93.23%, in case we use only raw sensor signals, was attained when all 6 sensor signals were combined. The recognition rate of 92.22% was reached if the estimated trajectory was used as input. Finally we tested the ensemble method and the generalization rate of 95.04% was attained on the ensemble recognizer consisting of 3 FDA recognizers based on acceleration-only, angular-velocity-only and handwriting trajectory respectively.

Recognition of Grip-Patterns by Using Capacitive Touch Sensors

A novel and intuitive way of accessing applications of mobile devices is presented. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensor system is carefully designed and installed underneath the housing of the mobile device to capture the information of the users grip-pattern. The captured data is then recognized by a minimum distance classifier and a naive Bayes classifier. The recognition test is performed to validate the feasibility of the proposed user interface system

A novel hand gesture input device based on inertial sensing technique

In this paper, we present a novel gesture-based input device by using inertial sensing technique. The trajectories of users hand-drawn gestures in 3D space are captured and recognized by this device to fulfill user interaction task. The proposed device employs gyro-free inertial measurement unit (IMU) to track hand motions without requiring any external reference sensors or signals. Since the unbounded growing error of trajectory estimation, as a major drawback of IMU-based motion tracking technology, is carefully solved by using zero velocity compensation. Here, a deliberately-designed motion detection scheme is proposed to capture accurate hand motion period. Finally, the recognition algorithm based on Bayesian networks takes estimated trajectories and finds the corresponding gesture model with the maximum probability. Because the IMU provides outstanding capability of self-contained positioning, the proposed device is extraordinary simple and effective, comparing with the devices using other tracking technologies such as vision-based system. Experimental results also show its effectiveness and feasibility. Currently, after employing the trajectory estimation method provided in this paper, the recognition rate of 95.51% for 14 gestures has been achieved when this device is implemented as a TV remote controller. It can be used as a powerful, flexible interface for ubiquitous computing device.

GripLaunch: a Novel Sensor-Based Mobile User Interface with Touch Sensing Housing

This paper describes a novel way of applying capacitive sensing technology to a mobile user interface. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensing system is carefully designed and installed underneath the housing of the mobile device to capture the information of the users grip-pattern. The captured data is then recognized by dedicated recognition algorithms. The feasibility of the proposed user interface system is thoroughly evaluated with various recognition tests.