Classification of Grasp Types through Wavelet Decomposition of EMG Signals

Kakoty, Nayan M.; Hazarika, Shyamanta M.

doi:10.1109/bmei.2009.5305493

Cited by 8 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[81, p. 251]. This includes: -neural networks in different compositions [39], [93], [45], [47], [36], [8], [23], [42] [32], [4], [6], [90], [31], [30], [82], [86], including such based on adaptive resonance theory [91], -support vector machines and variants [49], [39], [48], [84], [66], [108], [90], [16], [86], -decision trees [30], -(naïve) Bayesian classification [103], [70], [52], [90], -fuzzy logic approaches [69], [3], -Gaussian mixture models [44], [106], [46], -logistic regression [30], -logistic model trees [30], -classification via independent component analysis (ICA) [93], canonical discriminant analysis [71], -linear discriminant analysis (LDA) [23], [22] [6], [31], [46], …”

Section: Related Workmentioning

confidence: 99%

“…The following features and transformations have proven well in the context of pattern-recognition-based myoelectric control (cf. [81, p. 250-251]): -linear envelope [107], [104, p. 271], [76], [10], -zero crossings and variance [87], -integral absolute value, variance, zero crossing [94], -mean absolute value [6], its slope, wave form length, number of waveform slope sign changes, number of waveform zero crossings (Hudgins set of features) [45], -frequency spectrum via Fourier transform [26], [39], [93], random Fourier features [35], [34], as well as local frequency and phase content via short-time Fourier transform [41], [23,22], [91], -autoregressive coefficients [103], [14], [55], -cepstral coefficients [103], [14], -wavelet decomposition coefficients [23,22], [47], [67], [36], [8], [48], [84] and their Eigenvalues [66], -wavelet packet feature sets [23,22], motor unit action potentials (MUAPs) via wavelet packet transform and fuzzy C-means clustering [85], -signal energy (overall, within Hamming windows, within trapezoidal windows) as temporal features and spectral magnitude as well as spectral moments from short-time Thompson transform [91], -moving approximate entropy [2], andcontraction factors from fractal modeling [55], fractal dimensions [...…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Prototype Reduction on sEMG Data for Instance-based Gesture Learning towards Real-time Prosthetic Control

Sziburis

Nowak

Brunelli

2021

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies

View full text Add to dashboard Cite

This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices.The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined.Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself promising when applying kNN on the reduced set.A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier Features (RR-RFF). The kNN-based methods performed significantly better (𝑝 < 0.0005) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study.With 𝑘 = 1, which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prototype Reduction on sEMG Data for Instance-based Gesture Learning towards Real-time Prosthetic Control

Sziburis

Nowak

Brunelli

2021

Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies

View full text Add to dashboard Cite

show abstract

“…Consequently EMG for each of the grasp types is the composite of different frequency components. We used SWC as the feature for classification of grasp types [28].…”

Section: Feature Setmentioning

confidence: 99%

Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

Kakoty

Kaiborta

Hazarika

2012

IJRA

Self Cite

View full text Add to dashboard Cite

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five<br />fingered robotic hand to emulate six grasp types used during 70% daily living activities.<br /><br />

show abstract

Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems

Sziburis,

Nowak,

Brunelli

2023

Med Biol Eng Comput

View full text Add to dashboard Cite

This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques’ adequacy for embedded control applications regarding accuracy and timing behaviour, decision surface mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against ridge regression (RR) and RR with random Fourier features (RR-RFF). The kNN-based methods performed significantly better ($$p < 0.0005$$ p < 0.0005 ) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With $$k=1$$ k = 1 , which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications. Graphical abstract

show abstract

Classification of Grasp Types through Wavelet Decomposition of EMG Signals

Cited by 8 publications

References 22 publications

Prototype Reduction on sEMG Data for Instance-based Gesture Learning towards Real-time Prosthetic Control

Prototype Reduction on sEMG Data for Instance-based Gesture Learning towards Real-time Prosthetic Control

Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems

Contact Info

Product

Resources

About