| 1. |
- Kanitz, Gunter, et al.
(författare)
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Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses
- 2018
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : IEEE. - 1534-4320 .- 1558-0210. ; 26:9, s. 1756-1764
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.
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| 2. |
- Kanitz, Gunter, et al.
(författare)
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Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.
- 2011
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Ingår i: [Host publication title missing]. - 1557-170X. - 9781424441211 ; , s. 1608-1611
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Konferensbidrag (refereegranskat)abstract
- In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies.
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| 3. |
- Malešević, Nebojša, et al.
(författare)
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Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM)
- 2017
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Ingår i: 2017 International Conference on Rehabilitation Robotics, ICORR 2017. - 9781538622964 ; , s. 1518-1523
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.
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| 4. |
- Malešević, Nebojša, et al.
(författare)
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Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals
- 2018
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Ingår i: Complexity. - : Hindawi Limited. - 1076-2787 .- 1099-0526. ; 2018
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Tidskriftsartikel (refereegranskat)abstract
- We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.
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