A fusion of time-domain descriptors for improved myoelectric hand control

Khushaba, Rami N.; Al-Ani, Ahmed; Al-Timemy, Ali H.; Al-Jumaily, Adel

doi:10.1109/ssci.2016.7850064

Cited by 32 publications

(54 citation statements)

References 26 publications

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“…Finally, the code can be extended easily with new and innovative signal features thus also enlarging the code base of the system. Currently, we are extending it to include and parallelize the fused Time-Domain Descriptors signal feature extraction (fTDD, which demonstrated excellent performance for the classification of hand movements in sEMG data, Khushaba et al, 2016), but future contributions from other research groups are also welcome and can be useful to parallelize and accelerate most signal feature extraction procedures.…”

Section: Discussionmentioning

confidence: 99%

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

Atzori

Müller

2019

Front. Neurorobot.

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Motivation: Hand amputations can dramatically affect the quality of life of a person. Researchers are developing surface electromyography and machine learning solutions to control dexterous and robotic prosthetic hands, however long computational times can slow down this process.Objective: This paper aims at creating a fast signal feature extraction algorithm that can extract widely used features and allow researchers to easily add new ones.Methods: PaWFE (Parallel Window Feature Extractor) extracts the signal features from several time windows in parallel. The MATLAB code is publicly available and supports several time domain and frequency features. The code was tested and benchmarked using 1,2,4,8,16,32, and 48 threads on a server with four Xeon E7- 4820 and 128 GB RAM using the first 5 datasets of the Ninapro database, that are recorded with different acquisition setups.Results: The parallel time window analysis approach allows to reduce the computational time up to 20 times when using 32 cores, showing a very good scalability. Signal features can be extracted in few seconds from an entire data acquisition and in <100 ms from a single time window, easily reducing of up to over 15 times the feature extraction procedure in comparison to traditional approaches. The code allows users to easily add new signal feature extraction scripts, that can be added to the code and on the Ninapro website upon request.Significance: The code allows researchers in machine learning and biosignals data analysis to easily and quickly test modern machine learning approaches on big datasets and it can be used as a resource for real time data analysis too.

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Section: Discussionmentioning

confidence: 99%

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

Atzori

Müller

2019

Front. Neurorobot.

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“…In EMG pattern recognition, different classifiers are widely used. These are convolutional neural networks (CNNs) [ 42 , 43 ], artificial neural networks (ANNs) [ 1 , 44 ], linear discriminant analysis (LDAs) [ 45 ], support vector machines (SVMs) [ 46 , 47 ], and k-nearest neighbors (KNNs) [ 48 , 49 ]. Among these classifiers, the CNN provides better EMG recognition performance but requires a higher time for learning the model [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Ahmad

Haque

et al. 2021

Diagnostics

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A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

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“…We also consider that good results in experiments with able-bodied subjects may nevertheless yield poor results when transferred to amputees. Thus, we refer to the work of Campbell and colleagues [8] who found that classic features (mean absolute value, root mean square, zero crossing rate, slope sign changes and waveform length) and time-dependent power spectrum descriptors proposed in [9,10] (TD-PSD) remain functionally coherent when shifting from able-bodied participants to amputees. Thus, in our analysis we prioritize these features over others proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

Rasines

Prada²,

Bobrov³

et al. 2021

Applied Sciences

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This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

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A fusion of time-domain descriptors for improved myoelectric hand control

Cited by 32 publications

References 26 publications

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

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