Fuzzy Entropy-Based Muscle Onset Detection Using Electromyography (EMG)

Lyu, Ming; Xiong, Caihua; Zhang, Qin; He, Lei

doi:10.1007/978-3-319-13966-1_9

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…In contrast fuzzy entropy (FuzzyEn) [18] employs a continuous fuzzy decision boundary to give a relative degree of similarity, resulting in a more consistent measure of entropy which is less sensitive to parameter selection [19]. Furthermore, FuzzyEn has been shown to be an ef-fective measure of EMG data [18,20].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Jelfs

Tin

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The concept of muscle synergies has proven to be an effective method for representing patterns of muscle activation. The number of degrees of freedom to be controlled are reduced while also providing a flexible platform for producing detailed movements using synergies as building blocks. It has previously been shown that small components of movement are crucial to producing precise and coordinated movement. Methods which focus on the variance of the data make it possible to overlook these small components in the synergy extraction process. However, algorithms which address the inherent complexity in the neuromuscular system are lacking. To that end we propose a new nonnegative matrix factorization algorithm which employs a cross fuzzy entropy similarity measure, thus, extracting muscle synergies which preserve the complexity of the recorded muscular data. The performance of the proposed algorithm is illustrated on representative EMG data.

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Section: Introductionmentioning

confidence: 99%

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Jelfs

Tin

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A Systematic Investigation of Detectors for Low Signal-to-Noise Ratio EMG Signals

Yuvaraj

Raja²,

David³

et al. 2023

Preprint

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Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. This study, therefore, investigates the performance of thirteen existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: gaussian, laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. The results indicate that the Modified Hodges detector, a simplified version of the threshold-based Hodges detector introduced in the current study was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Overall, the Modified Hodges, Gaussian and Laplacian Generalized Likelihood ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

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