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

Nagata, Kentaro; Ando, Keiichi; Magatani, Kazushige; Yamada, Masafumi

doi:10.1109/iembs.2007.4353517

Cited by 20 publications

(15 citation statements)

References 11 publications

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“…Consequently, HD-FMG was compared with literature results consistent with the instrumentation of current commercially available pattern recognitionbased myoelectric control systems [12]. Furthermore, studies have shown that the use of more than eight channels of EMG does not significantly improve myoelectric classification accuracy above that reported here [29][30][31][32][33]. It would be preferable to directly compare HD-FMG and EMG signals and to possibly combine them, but the nature of the HD-FMG sensor instrumentation prohibited recording both signals simultaneously.…”

Section: Discussionmentioning

confidence: 99%

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

111

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Abstract-Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using highdensity force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%-11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

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Section: Discussionmentioning

confidence: 99%

High-density force myography: A possible alternative for upper-limb prosthetic control

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

111

View full text Add to dashboard Cite

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“…These recordings are used widely because the signal is noninvasively detected and it precedes the actual body movements [6], thus it is faster than kinematic and dynamic devices such as force sensors and motion trackers. Tenore et al and Nagata et al have demonstrated the possibilities of individually distinguishing the flexion and extension of fingers using sEMG and have suggested the potential of controlling the individual fingers of a hand robot [7,8]. They, however, only distinguished two states of movement, "ON" or "OFF", and did not try to extract information regarding force from the sEMG.…”

Section: Introductionmentioning

confidence: 94%

Real-time isometric pinch force prediction from sEMG

Choi

Kwon

Park

et al. 2008

2008 2nd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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This paper describes a real-time isometric pinch force prediction algorithm using surface electromyogram (sEMG). The activities of seven muscles related to the movements of the thumb and index finger joints, which are observable using surface electrodes, were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. From the seven muscles, four muscles were selected for monitoring using the Fisher linear discriminant paradigm in an off-line analysis, and the recordings from these four muscles provided the most effective information for mapping sEMG to the pinch force. An ANN structure was designed to perform efficient training and to avoid both under-fitting and over-fitting problems. Finally, the pinch force prediction algorithm was tested with five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.093 ±0.047 and CORR = 0.957 ±0.031. These results imply that the proposed algorithm is useful to measure the generated pinch force without force sensors. The possible applications of the proposed method include controlling bionic finger robot systems to overcome finger paralysis or amputation.

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“…The SEMG feature extraction is performed by an integrated time and that is set to 300 ms [9]. Now, each component of feature extraction i X is denoted by…”

Section: Motion Recognition With Linear Discriminant Analysis (Canmentioning

confidence: 99%

“…EMG can be used for a lot of studies (e.g., clinical, biomedical, basic physiological, and biomechanical studies) [1][2][3][4][5][6][7][8][9][10]. Recently, to describe the neuromuscular activation of muscles within functional movements, kinesiological electromyography deserves attention and is established as an evaluation tool for various applied research.…”

Section: Introductionmentioning

confidence: 99%

Estimation of muscle strength during motion recognition using multichannel surface EMG signals

Nagata

Nakano

Magatani

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The use of kinesiological electromyography is established as an evaluation tool for various kinds of applied research, and surface electromyogram (SEMG) has been widely used as a control source for human interfaces such as in a myoelectric prosthetic hand (we call them 'SEMG interfaces'). It is desirable to be able to control the SEMG interfaces with the same feeling as body movement. The existing SEMG interface mainly focuses on how to achieve accurate recognition of the intended movement. However, detecting muscular strength and reduced number of electrodes are also an important factor in controlling them. Therefore, our objective in this study is the development of and the estimation method for muscular strength that maintains the accuracy of hand motion recognition to reflect the result of measured power in a controlled object. Although the muscular strength can be evaluated by various methods, in this study a grasp force index was applied to evaluate the muscular strength. In order to achieve our objective, we directed our attention to measuring all valuable information for SEMG. This work proposes an application method of two simple linear models, and the selection method of an optimal electrode configuration to use them effectively. Our system required four SEMG measurement electrodes in which locations differed for every subject depending on the individual's characteristics, and those were selected from a 96ch multi electrode using the Monte Carlo method. From the experimental results, the performance in six normal subjects indicated that the recognition rate of four motions were perfect and the grasp force estimated result fit well with the actual measurement result.

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

Cited by 20 publications

References 11 publications

High-density force myography: A possible alternative for upper-limb prosthetic control

High-density force myography: A possible alternative for upper-limb prosthetic control

Real-time isometric pinch force prediction from sEMG

Estimation of muscle strength during motion recognition using multichannel surface EMG signals

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