Hyoyoung Jang

Advanced Soft Remote Control System Using Hand Gesture

In this paper, we propose an Advanced Soft Remote Control System so as to endow the users with the ability to control various home appliances instead of individual remote controller for each appliance and to command naturally at various places without being conscious of the view direction of the cameras. Through the developed system, the user first selects the device that he/her wants to control by pointing it with his/her hand. Then, the user can command operation of the desired functions via 10 predefined basic hand motion commands. By the experiment, we can get 97.1% recognition rate during offline test and 96.5% recognition rate during online test. The developed system complements some inconveniences of conventional remote controllers specially by giving additional freedom to persons with movement deficits and people without disabilities.

Two-staged hand-posture recognition method for Softremocon system

This paper deals with a method to recognize hand-postures in Softremocon system. The system uses multiple cameras to recognize the users hand-posture. It is hard to recognize hand-posture since a human-hand is the object with high degree of freedom and there follows the self-occlusion problem, the well-known problem in vision-based recognition area. It can be a possible solution to use multiple cameras. However, when we use multiple images, another problem is arisen, that is, the composition methods of multiple recognition results from each camera to get the final decision. We show 2-staged recognition process. The first stage gets the results which represent each camera viewpoint. And the second one results out the final decision. This structure gives robustness to the hand-posture recognition against view-point variation.

Walking Phase Recognition for People with Lower Limb Disability

This paper presents a total solution on EMG signal-based walking phase recognition for people with lower limb disability. Various environmental factors such as sensed location, walking speed, and ground inclination are taken into consideration in all the phases of signal sensing, feature extraction, feature selection, and classification. Based on analysis on fourteen well-known feature extraction methods in varying environmental situation, this paper proposes a methodology for selecting a good feature set, and then demonstrates effectiveness of the proposed approach with the classification results.

View-invariant hand-posture recognition for soft-remocon-system

The hand-posture-based user interface has been considered as an alternative to human-computer interaction (HCI), or for human-robot interaction, but many difficulties remain to be resolved. For natural recognition of hand posture by a vision device, we find that view-point-in variance is one of the most required characteristics. This paper presents a hand-posture recognition method that is robust against various viewpoints. Observing that when the viewing direction is changed, the same appearance feature does not necessarily match the 3D structure, and vice versa, we propose a 2-layered hand-posture database structure in which the 2D appearance-based method and the 3D-based method are complementarity combined. We have tested the approach in an experiment for 15 different viewing directions.

Human-friendly HCI method for the control of home appliance

This paper describes a HCI system for the control of home appliance which is focused on human friendliness. This system utilizes two USB cameras to enable a user to select home appliance easily by hand pointing gesture. We propose two different methods of storing the three dimensional position of home appliance by user. Home appliance selection method and feedback for users wrong pointing direction are also described in this paper. Because of the low cost installation, simple operation and interactive feedback, our proposed system enhances the usability and human-friendliness.

Soft Sign Language Expression Method of 3D Avatar

This paper proposes a 3D avatar which expresses sign language in a very using lips, facial expression, complexion, pupil motion and body motion as well as hand shape, Hand posture and hand motion to overcome the limitation of conventional sign language avatars from a deaf`s viewpoint. To describe motion data of hand and other body components structurally and enhance the performance of databases, we introduce the concept of a hyper sign sentence. We show the superiority of the developed system by a usability test through a questionnaire survey.

Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment

In this paper, an intelligent home environment with an adaptive type-2 fuzzy logic (AT2FL) method is developed. The considered system is mainly for elderly and disabled persons and catches the users’ intention through recognizing their hand gestures instead of remote controller. However, during the recognition procedure, to distinguish between the command and ordinary gestures needs a very rigorous procedure, and to cope with that, the AT2FL approach is proposed in this paper. The presented algorithm is applied to control home appliances, and some experimental results will be shown to verify the feasibility and effectiveness.

A study on vision-based robust hand-posture recognition by learning similarity between hand-posture and structure

This paper proposes a robust hand-posture recognition method by learning similarity between hand-posture and structure for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the users hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of users hand restricts the users freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and includes learning the similarity between the two types of features. The validity of the proposed method is evaluated by applying it to the hand-posture recognition system using three cameras.