Masafumi Yamada

A Classification Method of Hand Movements Using Multi Channel Electrode

In this study, we describe the classification method of hand movements using 96 channels matrix-type(16times6) of multi channel surface electrode. Today, there are many systems that use the EMG as a control signal. As for those ordinary systems, it has some problem like most of them require the definition of measuring position. We design the new system with multi channel electrode to solve some of those conventional problems. Our system that has 96 channels electrode does not need to select a particular electrode position. Only attaching this electrode, we can obtain correct EMG and this way means providing with a simple and easy way. The purpose of this study is development of the EMG pattern recognition method using multi channel electrode. From measured 96 channels EMG data, we chose one line (16channels) of this electrode with the smallest noise. The EMG signal is recognized by canonical discriminant analysis. In order to recognize the EMG signal, the first three eigenvectors are chosen to form a discriminant space. And Euclidean distance is applied to classify the EMG. From the experiment in this method, we can discriminate 12 movements of the hand including four finger movements. And the recognition rate that can be done in real-time was measured at 80 percent on the average

Development of the hand motion recognition system based on surface EMG using suitable measurement channels for pattern recognition

Conventional research on motion recognition using surface electromyogram (SEMG) is mainly focused on how to process with the signals for pattern recognition. However, it is of much consequence to the motion recognition that measurement channels position including useful information about SEMG pattern recognition is selected. In this paper, we present two topics for the hand motion recognition system based on SEMG. First described is the method to select the suitable measurement channels position of multichannel SEMG for the recognition of hand motion, and the second described is an applied systems based on our proposed method. About channel selection, we use a multichannel matrix-type surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those electrodes, system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. The recognition experiments of 18 hand motions show that the average rate was measured to be grater than 96%. And the number of selected channels ranged from 4 to 7. About applied systems, our developed system works as an input interface for the computer (keyboard and pointing devise) and a robot hand.

Development of the assist system to operate a computer for the disabled using multichannel surface EMG

The purpose of this study is development of the assist system to easily operate a computer for the disabled such as amputees. In order to operate a computer, we usually use a mouse, and our system assists a mouse operation. Our system requires electromyography (EMG) signals of the arm instead of the mouse operation by the hand, and those EMG signals are used to become a control source for the mouse operation. To acquire the EMG signal, using a multichannel electrode that has 96 channels is a key of this assist system. As for the old assist system using the EMG, most of them require the definition of the target muscle. In many cases, these systems used 2 or more electrodes. We have developed an assist system using 96 channels matrix-type surface multi channel electrode. The EMG signal is recognized by canonical discriminant analysis. From measured EMG data, we can discriminate 10 movements of the hand. So, our system will be a powerful one to support activities of the disabled.

Development of the input equipment for a computer using surface EMG

SEMG (surface EMG) has many benefits, for example measuring SEMG is easy and a characteristic pattern of SEMG is obtained for each different movement. Therefore, SEMG that is generated by body movement is able to use as a control signal for some electric powered equipments. Our objective is the perfect control of the computer by using SEMG that is generated from forearms. In this paper, we will talk about our developed interface system that works as a keyboard of the computer

Development of the human interface equipment based on surface EMG employing channel selection method

In this paper, we describe the human-interface equipment using surface electromyogram (SEMG) based on optimal measurement channels for each subject. In case the SEMG is used as a control signal, individual differences of SEMG are important issue to obtain high accuracy recognition of motions. To solve this problem, we propose a channel selection method of the suitable measurement channels for the recognition of motions. We use a 96-channel matrix-type (6times16) surface electrode attached to the forearm in order to measure the SEMG generated from many active muscles during hand motions. From those 96 electrodes, our system decided the number of measurement channels and the position of measurement channels. This can be achieved by using the Monte Carlo method. Our system generates 10,000 sets of randomly selected channels, and these sets are evaluated by the recognition rate of hand motions. One set that records a highest recognition rate is selected from 10,000 sets for an optimal set of measurement channels. And the one set with the smallest number of measurement channels which fulfil the recognition rate above 90% or the maximum recognition rate above 95% is used for real-time recognition. Six normal subjects were experimentally tested using our system. The recognition rates of 18 hand motions, including 10 finger movements, were assessed for every subject. We were able to distinguish all the motions, and the average recognition rate in the real-time experiment was measured to be greater than 95%. And the number of selected channels ranged from 4 to 7

Application of least square method for muscular strength estimation in hand motion recognition using surface EMG

In this study, we describe the application of least square method for muscular strength estimation in hand motion recognition based on surface electromyogram (SEMG). Although the muscular strength can consider the various evaluation methods, a grasp force is applied as an index to evaluate the muscular strength. Today, SEMG, which is measured from skin surface, is widely used as a control signal for many devices. Because, SEMG is one of the most important biological signal in which the human motion intention is directly reflected. And various devices using SEMG are reported by lots of researchers. Those devices which use SEMG as a control signal, we call them SEMG system. In SEMG system, to achieve high accuracy recognition is an important requirement. Conventionally SEMG system mainly focused on how to achieve this objective. Although it is also important to estimate muscular strength of motions, most of them cannot detect power of muscle. The ability to estimate muscular strength is a very important factor to control the SEMG systems. Thus, our objective of this study is to develop the estimation method for muscular strength by application of least square method, and reflecting the result of measured power to the controlled object. Since it was known that SEMG is formed by physiological variations in the state of muscle fiber membranes, it is thought that it can be related with grasp force. We applied to the least-squares method to construct a relationship between SEMG and grasp force. In order to construct an effective evaluation model, four SEMG measurement locations in consideration of individual difference were decided by the Monte Carlo method.

Monte carlo method for evaluating the effect of surface EMG measurement placement on motion recognition accuracy

Surface electromyogram (SEMG) is one of the most important biological signal in which the human motion intention is directly reflected. Many systems use SEMG as a source of a control signal. (We call them “SEMG system”). In order to develop SEMG system, constructions of discriminant function and SEMG measurement placement are important factors for accurate recognition. But standard criterions for selection of discriminant function and SEMG measurement placement have not been clearly defined. Almost all of the conventional SEMG system has decided to select measurement placements of SEMG according to standard general anatomical structure of the human body and that mainly focused on signal processing method. However, SEMG measurement placement is also critical for recognition accuracy and evaluating the effect of SEMG measurement placement is important. In this study, we investigate the effect of SEMG measurement placement in hand motion recognition accuracy. We use a 96-channels matrix-type surface multielectrode and four channels are selected as the SEMG measurement placements from the channels that compose multielectrode. 5,000 configurations of SEMG measurement placements are generated by randomly selected number and each configuration is assessed by motion recognition accuracy (i.e. Monte Carlo method). In order to consider the influence of discriminant analysis, our system employs the linear discriminant analysis and nonlinear discriminant analysis. Each selected SEMG measurement placement is evaluated by those two types of discriminant analysis and the results are compared with each other. The experimental results show that motion recognition accuracy differs between these two analyses even if the same SEMG measurement placement is used. Not all optimal measurement placements for linear discriminant function suit for nonlinear discriminant function. The outcome of these investigations, the SEMG measurement placement should be taken into consideration and it suggests the necessity of evaluating the optimal measurement placement depending on a discernment analysis.

Extraction and Analysis of the Single Motor Unit F-Wave of the Median Nerve

The matrix type multielectrodes have been proved to be useful for getting the information of motor unit (MU) properties (Monster et al., 1980; Reucher et al., 1987; Yamada et al., 1987; Masuda and Sadoyama, 1988; Kleine et al., 2000; Gazzoni et al., 2004). By this electromyography (EMG) technique, we can obtain many essential properties of MU that could not be obtained by conventional surface EMG and/or needle EMG. One of them is the waveform property of motor unit action potential (MUAP) which is enabled by extracting the single propagating MUAP. Another is the two-dimensional location of motor end-plates. There have been many applications of multichannel surface EMG for measuring voluntary muscle activities, but few for evoked EMG responses. However, recent studies have demonstrated the applications of the multichannel surface EMG for the measurement of muscle fiber conduction velocities (MFCVs) (Metani et al., 2005) , for the estimation of firing thresholds (Yamada, 2004), and for the motor unit number estimate (MUNE) (Blok et al., 2005; van Dijk et al., 2008). The F-wave represents a long latency response detected from a muscle following stimulation of a peripheral nerve at supramaximal intensity. The F-waves are produced by antidromic activation of motor neurons. The latency and the waveform of the F-wave change with each stimulus. The F-wave studies are widely used in clinical neurophysiology as a quantitative estimation method of the objective pathological changes. The F-waves that consist of a single MUAP occur also with submaximal stimulation. Low intensity stimulation could increase the probability of the single MU F-wave (Komori et al., 1991; Doherty et al., 1994; Stashuk et al., 1994; Yamada, 2004), and reduce the discomfort of the subjects. Therefore, different MUAPs can be readily analyzed by classifying F-waves. Many studies concerned with the single MU F-wave were performed for the estimation of the conduction velocity of nerve fibers (Doherty et al., 1994; Felice, 1998; Wang et al., 1999) and the MUNE (Stashuk et al., 1994; Hara et al., 2000). The conventional surface EMG technique was used in these studies, and there were few applications of multichannel surface EMG for F-wave studies. In a previous study, we investigated the classification and analysis of multichannel bipolar F-waves (Yamada et al., 2007). It is difficult to detect deep MUs from the bipolar EMG. So, in the present study, the thenar MUs F-waves were investigated by extracting single MU Fwaves from monopolar multichannel surface EMG signals. By increasing the number of