Decoding of Individuated Finger Movements Using Surface Electromyography

Tenore, Francesco V.; Ramos, Ander; Fahmy, Ahmed S.; Acharya, Soumyadipta; Etienne‐Cummings, Ralph; Thakor, Nitish V.

doi:10.1109/tbme.2008.2005485

Cited by 303 publications

(197 citation statements)

References 20 publications

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“…In this case the situation seems much better even for amputees: residual muscle activity of excellent quality has recently been found in long-term amputees [30,10,33,13].…”

Section: Applicationsmentioning

confidence: 98%

“…Some results mapping sEMG activity to finger, wrist and arm position have actually appeared, e.g. in [35,33,37], but the assumption there is that sEMG relates to isotonic/isometric muscle configurations which, in free movement, can roughly be associated to positions. Here we are concerned with grasping, therefore those results are not relevant to this work; when dynamic interaction with the environment (e.g., objects to be grasped) comes into play, any such trivial relationship is likely to be broken down.…”

Section: Introductionmentioning

confidence: 99%

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Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas so far the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e., that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.

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“…In this case the situation seems much better even for amputees: residual muscle activity of excellent quality has recently been found in long-term amputees [30,10,33,13].…”

Section: Applicationsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Evidence of muscle synergies during human grasping

Castellini

Smagt

2013

Biol Cybern

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“…The sensor array is positioned on the proximal forearm near the elbow, to monitor activation of the major forearm muscles. These EMG signals contain a combination of force and position information for the wrist, hand and fingers [8].…”

Section: A Biosleeve Hardwarementioning

confidence: 99%

“…In short term lab tests, greater than 90% classification accuracy on up to 12 static hand gestures is reported [6], [7]. For the control of dexterous actions, forearm EMG has been shown to provide accurate representations of finger movements and forces [8]. Hand and individual finger tracking from a small forearm EMG array were previously demonstrated at NASA Ames for virtual keypads and joysticks [9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Investigating evolvable hardware classification for the BioSleeve electromyographic interface

Glette

Kaufmann

Assad

et al. 2013

2013 IEEE International Conference on Evolvable Systems (ICES)

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Abstract-We investigate the applicability of an evolvable hardware classifier architecture for electromyography (EMG) data from the BioSleeve wearable human-machine interface, with the goal of having embedded training and classification. We investigate classification accuracy for datasets with 17 and 11 gestures and compare to results of Support Vector Machines (SVM) and Random Forest classifiers. Classification accuracies are 91.5% for 17 gestures and 94.4% for 11 gestures. Initial results for a field programmable array (FPGA) implementation of the classifier architecture are reported, showing that the classifier architecture fits in a Xilinx XC6SLX45 FPGA. We also investigate a bagging-inspired approach for training the individual components of the classifier with a subset of the full training data. While showing some improvement in classification accuracy, it also proves useful for reducing the number of training instances and thus reducing the training time for the classifier.

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“…Some studies with amputees using few number of electrodes have been conducted in order to fulfil this gap, such as done in Al-Timemy et al (2013), Cipriani et al (2011), Li et al (2011, Kumar et al (2013) and Tenore et al (2009). In particular, in Kumar et al (2013) a method based on wavelet maxima density was proposed as a non-linear parameter to extract relevant from sEMG signals using only one channel, but no grasp gestures were considered.…”

Section: Introductionmentioning

confidence: 99%

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

et al. 2017

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Introduction: Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods: The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi's fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results: The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion:The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

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Decoding of Individuated Finger Movements Using Surface Electromyography

Cited by 303 publications

References 20 publications

Evidence of muscle synergies during human grasping

Evidence of muscle synergies during human grasping

Investigating evolvable hardware classification for the BioSleeve electromyographic interface

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

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