Conjugate-Prior-Penalized Learning of Gaussian Mixture Models for Multifunction Myoelectric Hand Control

Chu, Jun-Uk; Lee, Yun‐Jung

doi:10.1109/tnsre.2009.2015177

Cited by 29 publications

(7 citation statements)

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“…Additionally, the use of low-density sEMG signals represents an important advantage for the acceptance of prostheses by amputees, according with Khushaba et al (2012), who state that reduction in the number of electrodes, without compromising the classification accuracy, would significantly simplify the requirements for controlling state of the art prostheses. From the studies which considered dexterous gestures, Chu and Lee (2009) only included two grasp gestures (cylindrical and lateral grasps). On the other hand, Khezri and Jahed (2007) identified six gestures combined hand and wrist gestures, and only one grasp gesture is considered in comparison with five gestures reported in our study.…”

Section: Discussionmentioning

confidence: 99%

“…However, such attempts did not included more complex dexterous movements, in which statistical-based features are not sufficiently reliable due to the weak signals from these movements. Previous works did consider sEMG signals from dexterous movements, but only for grasp gestures, aiming to improve the functionality of the prosthetic control (Chu and Lee, 2009;Hargrove et al, 2009;Khezri and Jahed, 2007;Tommasi et al, 2013;Wang et al, 2013). In fact, the non-linear relationship between force and electric activity of muscles at low-levels of contraction (Naik et al, 2010) makes much more difficult the sEMG signals analysis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…A global search method, referred to as a conjugate-prior-penalized learning algorithm, was proposed for the learning of GMMs (Chu and Lee 2009). This learning method provided stable parameter and model order estimates using the conjugate priors of the GMM.…”

Section: Gaussian Mixture Model Learningmentioning

confidence: 99%

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction

et al. 2013

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Cutaneous afferent activities recorded by a nerve cuff electrode have been used to detect the stance phase in a functional electrical stimulation system for foot drop correction. However, the implantation procedure was difficult, as the cuff electrode had to be located on the distal branches of a multi-fascicular nerve to exclude muscle afferent and efferent activities. This paper proposes a new gait phase detection scheme that can be applied to a proximal nerve root that includes cutaneous afferent fibers as well as muscle afferent and efferent fibers. To test the feasibility of this scheme, electroneurogram (ENG) signals were measured from the rat sciatic nerve during treadmill walking at several speeds, and the signal properties of the sciatic nerve were analyzed for a comparison with kinematic data from the ankle joint. On the basis of these experiments, a wavelet packet transform was tested to define a feature vector from the sciatic ENG signals according to the gait phases. We also propose a Gaussian mixture model (GMM) classifier and investigate whether it could be used successfully to discriminate feature vectors into the stance and swing phases. In spite of no significant differences in the rectified bin-integrated values between the stance and swing phases, the sciatic ENG signals could be reliably classified using the proposed wavelet packet transform and GMM classification methods.

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“…Different features have been used in pattern recognition involving both time domain and time-frequency domain features. Some of these include mean absolute value [11,12,15-17], zero crossings (ZC) [11,12,15-17], slope sign changes (SSC) [11,12,15,16], autoregressive (AR) model coefficients [12,15,18-20], cepstrum coefficients [19], waveform length (WL) [11,12,16,17] and wavelet packet transform[13-15]. …”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies have been done to classify the features extracted from the sEMG like neural networks [11,20,21], bayesian classifier [24], linear discriminant analysis [16,23], hidden markov model [26], multilayer perceptron [13,14,23], fuzzy classifier [15,17-19], gaussian mixture model [12] and support vector machines (SVM) [21,22,27,28]. …”

Section: Introductionmentioning

confidence: 99%

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

2010

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BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.

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Conjugate-Prior-Penalized Learning of Gaussian Mixture Models for Multifunction Myoelectric Hand Control

Cited by 29 publications

References 15 publications

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

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

Gait phase detection from sciatic nerve recordings in functional electrical stimulation systems for foot drop correction

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

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