Wavelet Packet Entropy Based Control of Myoelectric Prosthesis

Iqbal, Nisheena V.; Subramaniam, Kamalraj; Asmi, P Shaniba

doi:10.13005/bpj/1382

Cited by 4 publications

(2 citation statements)

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“…Similarly, nonlinear features have been found to outperform linear features (only AR features performed well inter-level), with power spectral entropy, detrended fluctuation analysis (DFA) [77], and maximum fractal length achieving the highest performances in isolation [195]. Nonlinear features were further explored by Iqbal et al [196], with entropy features derived from the wavelet packet transform outperforming those from DFA.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly, nonlinear features have been found to outperform linear features (only AR features performed well inter-level), with power spectral entropy, detrended fluctuation analysis (DFA), and maximum fractal length achieving the highest performances in isolation [199]. Nonlinear features were further explored by Iqbal et al [200], with entropy features derived from the wavelet packet transform outperforming those from DFA.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Preprint

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Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis

Iqbal

Subramaniam

Asmi

2019

Biomedical Signal Processing and Control

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Wavelet Packet Entropy Based Control of Myoelectric Prosthesis

Cited by 4 publications

References 0 publications

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis

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