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Daisuke Nishikawa

Bio: Daisuke Nishikawa is an academic researcher from Hokkaido University. The author has contributed to research in topics: Signal & Reconfigurable computing. The author has an hindex of 8, co-authored 12 publications receiving 443 citations.

Papers
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Proceedings ArticleDOI
12 Oct 1999
TL;DR: The effectiveness of the real-time learning method that is defined as simultaneously controlling a prosthetic hand and learning to adapt to the operator's characteristics is confirmed by these experiments.
Abstract: This paper reports the prosthetic hand controller discriminating ten forearm motions from two channels of EMG signals. The controller uses the real-time learning method that is defined as simultaneously controlling a prosthetic hand and learning to adapt to the operator's characteristics. In this method, the controller is divided into three units. The analysis unit extracts useful information for discriminating motions from EMG. The adaptation unit learns the relation between EMG and control command and adapts to the operator's characteristics. The trainer unit generates training data and makes the adaptation unit learn in real-time. In experiments, the proposed controller performs discriminating a maximum of ten forearm motions including four wrist motions and six hand motions. In an eight forearm motions experiment, the five subjects' average discriminating rate, which serves an index of accurate controlling, was 85.1%. Two groups occur from this result, one marks a high performance (91.7%) and another does not (75.2%). The paper discusses the factors of this difference in performance from both phases of training and reasons that the low proficiency leads to undesirable results in the latter groups. Besides, in the ten forearm motions experiment the average discriminating rate of three subjects who achieve high performance in the previous experiment was 91.5%. This paper concludes that the effectiveness of the real-time learning method is confirmed by these experiments.

101 citations

Book ChapterDOI
23 Sep 1998
TL;DR: This paper describes an integrated EHW LSI chip that consists of GA hardware, reconfigurable hardware logic, a chromosome memory, a training data memory, and a 16-bit CPU core (NEC V30) and an application of this chip is also described in a myoelectric artificial hand, which is operated by muscular control signals.
Abstract: The advantage of Evolvable Hardware (EHW) over traditional hardware is its capacity for dynamic and autonomous adaptation, which is achieved through by Genetic Algorithms (GAs). In most EHW implementations, these GAs are executed by software on a personal computer (PC) or workstation (WS). However, as a wider variety of applications come to utilize EHW, this is not always practical. One solution is to have the GA operations carried out by the hardware itself, by integrating these together with reconfigurable hardware logic like PLA (Programmble Logic Array) or FPGA (Field Programmable Gate Array) on to a single LSI chip. A compact and quickly reconfigurable EHW chip like this could service as an off-the-shelf device for practical applications that require on-line hardware reconfiguration. In this paper, we describe an integrated EHW LSI chip that consists of GA hardware, reconfigurable hardware logic, a chromosome memory, a training data memory, and a 16-bit CPU core (NEC V30). An application of this chip is also described in a myoelectric artificial hand, which is operated by muscular control signals. Although, work on using neural networks for this is being carried out, this approach is not very promising due to the long learning period required for neural networks. A simulation is presented showing that not only is the EHW performance slightly better than with neural networks, but that the learning time is considerably reduced.

81 citations

Journal ArticleDOI
TL;DR: Using this mechanism, a suitable mapping function of the surface electromyogram (EMG) to motions of prosthetic hands can be acquired according to the amputee's evaluation in practical use and realizes a shortening of training time and adaptation to individual variation in real time.
Abstract: This paper proposes a novel learning method for prosthetic hand control. Conventional works have used off-line learning methods for control, and hence two kinds of training must be carried out separately: one is for the amputee to control prosthetic hands, and the other is for the prosthetic hand controller to adapt to the amputee's variations. Consequently, an amputee cannot acquire the sensations of operating prosthetic hands through training, and nevertheless he or she is likely to experience difficulties in forcing the prosthetic hand controller to follow the change of his or her own characteristics in practical use. We accordingly design an on-line learning mechanism which can adapt to the individual's characteristics in real time. Using this mechanism, a suitable mapping function of the surface electromyogram (EMG) to motions of prosthetic hands can be acquired according to the amputee's evaluation in practical use. Thereby, the mechanism realizes a shortening of training time and adaptation to individual variation in real time. The experiments succeeded in discriminating six forearm motions to verify the proposed method. First, we use intrinsic exercise images to control a prosthetic hand, and compare our on-line method with one conventional off-line method. Second, we use EMG signals on shoulder girdles to control the prosthetic hand for upper elbow amputation. The discrimination rate in forearm EMG experiments is 89.9% by our method and 80.3% by the conventional method. Moreover, we show the possibility of applying the on-line learning method to upper elbow amputees, because a discrimination rate of 79.3% is achieved by our method in shoulder girdle EMG classification. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(10): 35–46, 2001

68 citations

Proceedings ArticleDOI
17 Oct 1999
TL;DR: Experiments show that the proposed controller discriminates ten forearm motions, which contain four wrist motions and six hand motions, and learns within 4/spl sim/25 minutes, the average of the discriminating rate is 91.5%.
Abstract: We discuss the necessity of a learning mechanism for an EMG prosthetic hand controller, and the real-time learning method is proposed and designed. This method divides the controller into three units. The analysis unit extracts useful informations for discriminating motions from the EMG. The adaptation unit learns the relation between EMG and control command and adapts operator's characteristics. The trainer unit makes the adaptation unit learn in real-time. Experiments show that the proposed controller discriminates ten forearm motions, which contain four wrist motions and six hand motions, and learns within 4/spl sim/25 minutes. The average of the discriminating rate is 91.5%.

59 citations

Journal ArticleDOI
TL;DR: Results show that the proposed approaches can simplify decision boundaries, the attainment of motor skill can be used for judging completion of the training by external observers, and bottlenecks in this classifier can be detected.
Abstract: An on-line learning based EMG to motion classifier can manage learning data set by manual appending and automatic elimination compared with conventional off-line learning based classifiers It is designed to track the alteration of an operator's characteristics through time The automatic elimination is based on the continuity of human motion Moreover, in this study we quantify the attainment of motor skin using the classifier By classifying up to eight forearm motions from two channels of EMG, we investigate the effectiveness of the automatic elimination process, the validity of the attainment of motor skill by seven trials on an unskilled subject, as well as the relationship among the number of electrodes, the classification performance, and the subject's motor skill Results show that the proposed approaches can simplify decision boundaries, the attainment of motor skill can be used for judging completion of the training by external observers, and bottlenecks in this classifier can be detected

38 citations


Cited by
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Journal ArticleDOI
TL;DR: The various methodologies and algorithms for EMG signal analysis are illustrated to provide efficient and effective ways of understanding the signal and its nature to help researchers develop more powerful, flexible, and efficient applications.
Abstract: Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

1,195 citations

Journal ArticleDOI
TL;DR: An analytical and comparative survey of upper limb prosthesis acceptance and abandonment as documented over the past 25 years is presented, detailing areas of consumer dissatisfaction and ongoing technological advancements.
Abstract: This review presents an analytical and comparative survey of upper limb prosthesis acceptance and abandonment as documented over the past 25 years, detailing areas of consumer dissatisfaction and ongoing technological advancements. English-language articles were identified in a search of Ovid, PubMed, and ISI Web of Science (1980 until February 2006) for key words upper limb and prosthesis. Articles focused on upper limb prostheses and addressing: (i) Factors associated with abandonment; (ii) Rejection rates; (iii) Functional analyses and patterns of wear; and (iv) Consumer satisfaction, were extracted with the exclusion of those detailing tools for outcome measurement, case studies, and medical procedures. Approximately 200 articles were included in the review process with 40 providing rates of prosthesis rejection. Quantitative measures of population characteristics, study methodology, and prostheses in use were extracted from each article. Mean rejection rates of 45% and 35% were observed in the literature for body-powered and electric prostheses respectively in pediatric populations. Significantly lower rates of rejection for both body-powered (26%) and electric (23%) devices were observed in adult populations while the average incidence of non-wear was similar for pediatric (16%) and adult (20%) populations. Documented rates of rejection exhibit a wide range of variance, possibly due to the heterogeneous samples involved and methodological differences between studies. Future research should comprise of controlled, multifactor studies adopting standardized outcome measures in order to promote comprehensive understanding of the factors affecting prosthesis use and abandonment. An enhanced understanding of these factors is needed to optimize prescription practices, guide design efforts, and satiate demand for evidence-based measures of intervention.

902 citations

Journal ArticleDOI
TL;DR: A critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses is provided.
Abstract: Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strate- gies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.

802 citations

Journal ArticleDOI
TL;DR: The traditional methods used to control artificial hands by means of EMG signal are presented, in both the clinical and research contexts, and what could be the future developments in the control strategy of these devices are introduced.
Abstract: The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.

562 citations

Journal ArticleDOI
TL;DR: This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006, but it was brought to the attention of the journal and authors that reference 74 was incorrect.
Abstract: This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

515 citations