Hiroshi Yokoi

Bio: Hiroshi Yokoi is an academic researcher from University of Electro-Communications. The author has contributed to research in topics: Robot & Functional electrical stimulation. The author has an hindex of 26, co-authored 294 publications receiving 2642 citations. Previous affiliations of Hiroshi Yokoi include Hokkaido University & University of Tokyo.

Electrocorticographic control of a prosthetic arm in paralyzed patients

Abstract: Paralyzed patients would benefit from movement restoration afforded by electrocorticography (ECoG)–controlled prosthetics. However, it is unclear whether ECoG signals from chronically paralyzed patients provide sufficient motor information and, if they do, whether they can be used for prosthetic control.

Real-time control of a prosthetic hand using human electrocorticography signals: Technical note

Abstract: Object A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements. Methods A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analy...

EMG prosthetic hand controller using real-time learning method

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.

An active artificial whisker array for texture discrimination

Abstract: Whiskers are powerful sensors for robots that are not only useful for basic tasks such as obstacle avoidance, but also have the potential for gathering rich information about objects. We have developed an active multi-whisker array modeled on the rat whisker system which can be amounted on a mobile robot. We show that with this whisker array we can discriminate different textures based on the frequencies elicited by the whiskers. We exploit the phase-locked structure of our data using sensory-motor integration. Two factors enable better discrimination of the textures: firstly, considering several touch events from one whisker; and secondly, combining the information from more than one whisker.

A Gate-Level EHW Chip: Implementing GA Operations and Reconfigurable Hardware on a Single LSI

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.

Techniques of EMG signal analysis: detection, processing, classification and 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.