Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme, Erik; Englehart, Kevin

doi:10.1682/jrrd.2010.09.0177

Cited by 809 publications

(663 citation statements)

References 70 publications

(86 reference statements)

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“…Such a device could be used as input to pilot a real hand prosthesis [2], a virtual avatar [3] and for clinical/rehabilitative purposes [4] on healthy subjects, as well as on transradial upper-limb amputees, who currently utilize EMG-controlled devices to partially substitute missing arm functionalities. In order to improve the performances of the proposed setup and to explore new possibilities related to EMG classification problems, we evaluated two separate approaches to preprocess and classify EMG data.…”

Section: Discussionmentioning

confidence: 99%

“…Thanks to its noninvasiveness and ease in acquisition, sEMG signals can be exploited by many human-computer interaction (HCI) devices as input to control a prosthesis [2] or a virtual device [3], either for interactive or clinical/rehabilitative purposes [4,5]. The development of such HCI systems can address the needs of transradial amputees, who could greatly benefit from the resulting augmentation of stump functionalities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

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a b s t r a c tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human-computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for ("within" analysis) and to compare between ("between" analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users' gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

show abstract

“…This approach has been widely accepted because of its relative ease of implementation and high performance [11].…”

Section: Data Processing and Classifiermentioning

confidence: 99%

“…Pattern recognition algorithms have been widely investigated in terms of real-time implementation and performance [7,[10][11], and a pattern recognition-based control system has recently been commercially deployed [12]. Low classification errors, in the range of 2.2 to 11.3 percent, have been reported for varying numbers (6 to 10) of wrist and hand movements using EMG pattern recognition techniques, such as linear discriminant analysis (LDA), artificial neural networks, and support vector machines (SVMs) [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Radmand¹,

Scheme²,

Englehart³

2016

J Rehabil Res Dev

Self Cite

108

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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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“…Solutions that have been explored include prosthetic sockets incorporating methods of suspension [17], incompressible fluid [18], and struts arranged longitudinally with respect to the residual limb [19], so as to allow small movements of the prosthetic socket without altering the position of the electrodes. Studies towards the improvement of signal recognition [20], have also demonstrated a substantial improvement in the acquisition of the signal. However, there is a significant increase in the number of surface electrodes, which increases the discomfort of the users, and the training time in which they learn how to operate their prostheses.…”

Section: Myoelectric Control Systemmentioning

confidence: 99%

At-Home Computer-Aided Myoelectric Training System for Wrist Prosthesis

Vilouras

Heidari

Navaraj

et al. 2016

Haptics: Perception, Devices, Control, and Applications

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Abstract. Development of tools for rehabilitation and restoration of the movement after amputation can benefit from the real time interactive virtual animation model of the human hand. Here, we report a computer-aided training/learning system for wrist disarticulated amputees, using the open source integrated development environment called "Processing". This work also presents the development of a low-cost surface Electro-MyoGraphic (sEMG) interface, which is an ideal tool for training and rehabilitation applications. The processed sEMG signals are encoded after digitization to control the animated hand. Experimental results demonstrate the effectiveness of the sEMG control system in acquiring sEMG signals for real-time control. Users have also the ability to connect their prostheses with the training system and observe its operation for a more explicit demonstration of movements.

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Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Cited by 809 publications

References 70 publications

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

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

At-Home Computer-Aided Myoelectric Training System for Wrist Prosthesis

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