Inhyuk Moon

Bio: Inhyuk Moon is an academic researcher from Dong-eui University. The author has contributed to research in topics: Mobile robot & Wheelchair. The author has an hindex of 17, co-authored 49 publications receiving 1354 citations. Previous affiliations of Inhyuk Moon include Yonsei University & Osaka University.

A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand

Abstract: This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay

A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Abstract: Electromyographic (EMG) pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study was to develop an efficient feature- projection method for EMG pattern recognition. To this end, a linear supervised feature projection is proposed that utilizes a linear discriminant analysis (LDA). First, a wavelet packet transform (WPT) is performed to extract a feature vector from four-channel EMG signals. To dimensionally reduce and cluster the WPT features, an LDA, then, incorporates class information into the learning procedure, and identifies a linear matrix to maximize the class separability for the projected features. Finally, a multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of the LDA for WPT features, the LDA is compared with three other feature-projection methods. From a visualization and quantitative comparison, it is shown that the LDA produces a better performance for the class separability, plus the LDA-projected features improve the classification accuracy with a short processing time. A real-time pattern-recognition system is then implemented for a multifunction myoelectric hand. Experiments show that the proposed method achieves a 97.4% recognition accuracy, and all processes, including the generation of control commands for the myoelectric hand, are completed within 97 ms. Consequently, these results confirm that the proposed method is applicable to real-time EMG pattern recognition for multifunction myoelectric hand control.

Wearable EMG-based HCI for Electric-Powered Wheelchair Users with Motor Disabilities

Abstract: Electromyogram (EMG) signal generated by voluntary contraction of muscles is often used in rehabilitation devices because of its distinct output characteristics compared to other bio-signals. This paper proposes a wearable EMG-based human-computer interface (HCI) for electric-powered wheelchair users with motor disabilities by C4 or C5 level spinal cord injury. User expresses his intention as shoulder elevation gestures, which are recognized by comparing EMG signals acquired from the levator scapulae muscles with a preset threshold value. In this paper HCI command to control electric-powered wheelchair is made of combinations of left-, right-and both-shoulders elevation gestures. The proposed wearable HCI hardware consists of two active surface electrodes, a high-speed micro-controller, a Bluetooth module, and a battery. Experimental results using the wearable EMG based HCI and the electric-powered wheelchair developed show the proposed wearable EMG-based HCI is feasible for the users with severe motor disabilities.

Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces

Abstract: This paper proposes an intelligent robotic wheelchair with user-friendly human-computer interface (HCI) based on electromyogram (EMG) signal, face directional gesture, and voice. The user's intention is transfered to the wheelchair via the HCI, and then the wheelchair is controlled to the intended direction. Additionally, the wheelchair can detect and avoid obstacles autonomously using sonar sensors. By combining HCI into the autonomous functions, it performs safe and reliable motions while considering the user's intention. The experimental results in the crowded environment show that the proposed robotic wheelchair is feasible for the disabled and the elderly with severe motor disabilities.

Development of an intelligent guide-stick for the blind

Abstract: An intelligent guide stick for the blind was developed. It consists of an ultrasound displacement sensor, two DC motors, and a micro-controller. The total weight is 4.0kg, and the width and the height of the guide stick are 24 cm and 85 cm, respectively. Computer simulations were performed in order to find the traces of the guide stick at three different paths using an in-house Visual C/sup ++/ software. Actual experiments were also performed to compare with the computer simulation results. The difference between the actual experiment and the simulation was 1.19 cm in the straight path. However, the difference alter the first 90/spl deg/ turn was 9.3 cm and became 11.9 cm after the second 90/spl deg/ turn. Nevertheless, the intelligent guide stick followed the path of the road successfully avoiding the obstacle. The intelligent guide stick will help the blind travel with providing more convenient means of life.

Human-Robot Interaction: A Survey

Abstract: Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

Brain Computer Interfaces, a Review

Abstract: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

Surface Electromyography Signal Processing and Classification Techniques

Abstract: Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Smart wheelchairs: A literature review.

Abstract: — Several studies have shown that both children andadults benefit substantially from access to a means of indepen-dent mobility. While the needs of many individuals with disabil-ities can be satisfied with traditional manual or poweredwheelchairs, a segment of the disabled community finds it diffi-cult or impossible to use wheelchairs independently. To accom-modate this population, researchers have used technologiesoriginally developed for mobile robots to create “smart wheel-chairs.” Smart wheelchairs have been the subject of researchsince the early 1980s and have been developed on four conti-nents. This article presents a summary of the current state of theart and directions for future research. Key words: artificial intelligence, independent mobility, infra-red range finder, laser range finder, machine vision, powerwheelchairs, robotics, sonar, subsumption, voice control. INTRODUCTION Several studies have shown that both children andadults benefit substantially from access to a means ofindependent mobility, including power wheelchairs, man-ual wheelchairs, scooters, a nd walkers [1–2]. Independentmobility increases vocational and educational opportuni-ties, reduces dependence on caregivers and family mem-bers, and promotes feelings of self-reliance. For youngchildren, independent mobility serves as the foundationfor much early learning [1]. Nonambulatory children lackaccess to the wealth of stimuli afforded self-ambulatingchildren. This lack of exploration and control often pro-duces a cycle of deprivation and reduced motivation thatleads to learned helplessness [3].For adults, independent mobility is an importantaspect of self-esteem and plays a pivotal role in “aging inplace.” For example, if older people find it increasinglydifficult to walk or wheel themselves to the commode,they may do so less often or they may drink less fluid toreduce the frequency of urination. If they become unableto walk or wheel themselves to the commode and help isnot routinely available in the home when needed, a moveto a more enabling environment (e.g., assisted living) maybe necessary. Mobility limitati ons are the leading cause offunctional limitations among adults, with an estimatedprevalence of 40 per 1,000 persons age 18 to 44 and 188per 1,000 at age 85 and older [4]. Mobility difficulties arealso strong predictors of activities of daily living (ADL)and instrumental ADL disabi lities because of the need to