Jin-Woo Jung

Robotic smart house to assist people with movement disabilities

This paper introduces a new robotic smart house, Intelligent Sweet Home, developed at KAIST in Korea, which is based on several robotic agents and aims at testing advanced concepts for independent living of the elderly and people with disabilities. The work focuses on technical solutions for human-friendly assistance in motion/mobility and advanced human-machine interfaces that provide simple control of all assistive robotic systems and home-installed appliances. The smart house concept includes an intelligent bed, intelligent wheelchair, and robotic hoist for effortless transfer of the user between bed and wheelchair. The design solutions comply with most of the users’ requirements and suggestions collected by a special questionnaire survey of people with disabilities. The smart house responds to the users commands as well as to the recognized intentions of the user. Various interfaces, based on hand gestures, voice, body movement, and posture, have been studied and tested. The paper describes the overall system structure and explains the design and functionality of some main system components.

Intention reading is essential in human-friendly interfaces for the elderly and the handicapped

Some major issues of human-friendly man-machine interaction/interfaces in an intelligent residential system are dealt with to cope with increasing needs of the elderly and the handicapped for a higher level of living conditions. Specifically, the existing systems in this area are first examined and the inadequacy of some current remote-control interfaces for the elderly/handicapped is pointed out. Then, some intention-reading techniques are introduced for the elderly and/or the physically handicapped, for easier and more convenient use of machines and engineering systems.

Advanced robotic residence for the elderly/the handicapped: realization and user evaluation

A novel advanced robotic residence, Intelligent Sweet Home (ISH), is developed at KAIST, Korea for testing advanced concepts for independent living of the elderly and the physically handicapped. The work focuses on human-friendly technical solutions for motion/mobility assistance, health monitoring, and advanced human-machine interfaces that provide easy control of both assistive devices and home-installed appliances. To improve the inhabitants comfort, an intelligent bed, intelligent wheelchair and mechatronic transfer robot were developed. And, various interfaces based on hand gestures and voice, and health monitoring system were studied. This paper emphasizes the realization scheme of Intelligent Sweet Home and user evaluation by a physically handicapped person.

Dynamic-footprint based person identification using mat-type pressure sensor

Many diverse methods have been developing in the field of biometric identification as human-friendliness has been emphasized in the intelligent systems area. And one of emerging method is to use human walking behavior. But, in the previous methods based on human gait, stable somewhat long-term walking data are an essential condition for person recognition. Therefore, these methods are difficult to cope with various change of walking velocity which may be generated frequently during real walking. In this paper, we suggest a new method which uses just one-step walking data from mat-type pressure sensor. When a human walk through the pressure sensor, we get quantized COP (center of pressure) trajectory and HMM (hidden Markov model) is used to make probability models for users each foot. And then, HMMs for two feet are combined for better performance by Levenberg-Marquart learning method. Finally, we prove the usefulness of the suggested method using 8 people recognition experiments.

Robust EMG pattern recognition to muscular fatigue effect for powered wheelchair control

The main goal of this paper is to design an electromyogram (EMG) pattern classifier which is robust against muscular fatigue effects for powered wheelchair control. When a user operates a powered wheelchair using EMG-based interface for a long time, muscular fatigue often arises from sustained duration of muscle contraction. The recognition rate thus is degraded and controlling wheelchair gets more difficult. In this paper, an important observation is addressed that the variations of feature values due to the effect of the muscular fatigue are consistent for sustained duration. Based on this observation, we design a robust pattern classifier through the adaptation process of hyperboxes of Fuzzy Min-Max Neural Network. We present, as a result, a significantly improved performance in terms of the continuous usage of wheelchair.

Dynamic footprint-based person recognition method using a hidden markov model and a neural network

Many diverse methods have been developed in the field of biometric identification as a greater emphasis is placed on human friendliness in the area of intelligent systems. One emerging method is the use of footprint shape. However, in previous research, there were some limitations resulting from the spatial resolution of sensors. One possible method to overcome this limitation is through the use of additional and independent information such as gait information during walking. In this study, we suggest a new person‐recognition scheme based on the center of pressure (COP) trajectory in the dynamic footprint. To make an efficient and automated footprint‐based person recognition method using the COP trajectory, we use a hidden Markov model and a neural network. Finally, we demonstrate the usefulness of the suggested method, obtaining an approximately 80% recognition rate using only the COP trajectory in our experiment with 11 people.

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.

Robust EMG pattern recognition to muscular fatigue effect for human-machine interaction

The main goal of this paper is to design an electromyogram (EMG) pattern classifier which is robust to muscular fatigue effects for human-machine interaction. When a user operates some machines such as a PC or a powered wheelchair using EMG-based interface, muscular fatigue is generated by sustained duration time of muscle contraction. Therefore, recognition rates are degraded by the muscular fatigue. In this paper, an important observation is addressed: the variations of feature values due to muscular fatigue effects are consistent for sustained duration time. From this observation, a robust pattern classifier was designed through the adaptation process of hyperboxes of Fuzzy Min-Max Neural Network. As a result, significantly improved performance is confirmed.

Design of a Gait Phase Recognition System That Can Cope With EMG Electrode Location Variation

Electromyogram (EMG) signal-based gait phase recognition for walking-assist devices warrants much attention in human-centered system design as it well exemplifies human-in-the-loop control where the systems prediction directly affects subsequent walking motion. Since walking motion poses considerable variations in electrode placement, performance reliability of such systems is contingent on a combination of electrode montage and a feature extraction method that takes into account underlying physiological factors of peripheral muscles where electrodes are placed. In many practical applications, however, proper consideration of effects of the electrode location variation on performance reliability of the system has received scant empirical attention. Here, based on a user-centered design principle, we establish a gait phase recognition system that is capable of rigidly controlling ill effects due to this covariate by carrying out a large-scale analysis that combines statistical, model-based, and empirical approaches. In doing so, we have developed a special sensing suit for the control of electrode placement and a reliable data acquisition. We then have conducted a nonparametric statistical analysis on class separability values of thirty types of EMG feature sets, followed by a model-based analysis to address the tradeoff between class separability and dimensionality. To further address the issue of how these results generalize to independent systems and data sets, we have carried out an empirical performance assessment over six classification methods. First, the two feature types, Integral of Absolute Value and Histogram, and a combination of the two are shown to be robust against electrode location variations while providing a firm performance guarantee. Second, system organization scenarios are presented on a case-by-case basis, allowing us to trade off system complexity for on-line adaptation capability. Collectively, our integrated analysis lends itself to formulating a guideline for design of highly reliable EMG signal-based walking assistant systems in a variety of smart home scenarios.

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.