Forearm movements classification of EMG signals using Hilbert Huang transform and artificial neural networks

Kukker, Amit; Sharma, Rajneesh; Malik, Hasmat

doi:10.1109/poweri.2016.8077417

Cited by 18 publications

(6 citation statements)

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“…Following the work in [44], R ranges from 0 to 6 with a step of 0.02. x N M [j] represents samples of the signal at rest. N is the total number of samples.…”

Section: Comparison With Machine Learning Methods: Lower Limb Motion ...mentioning

confidence: 99%

“…Existing research shows that transforming sEMG signals into time-frequency images and inputting them into CNN networks can improve recognition accuracy [41]. In previous studies, it was demonstrated that transforming sEMG signals into time-frequency images can be used for limb motion recognition [42], detection of neuromuscular diseases [43], recognition of static muscle fatigue [44,45], and muscle fatigue recognition during periodic dynamic contraction processes [46].…”

Section: Data Preprocessingmentioning

confidence: 99%

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Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System

Zhang,

Bai,

Yan

et al. 2024

Sensors

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Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user’s intention to move or muscle fatigue. These issues impede the user’s ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.

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“…Following the work in [44], R ranges from 0 to 6 with a step of 0.02. x N M [j] represents samples of the signal at rest. N is the total number of samples.…”

Section: Comparison With Machine Learning Methods: Lower Limb Motion ...mentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System

Zhang,

Bai,

Yan

et al. 2024

Sensors

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show abstract

“…Mean Frequency [32], [33] MNF -Sample Entropy [26], [30] SampEn m = 2, r = 0.2 × σ Difference Absolute Standard Deviation Value [33], [34] DASDV -Difference of Maximum and Minimum Value [4], [35] DMMV -Energy [12], [26] ENE -Hjorth1 (Activity) or Variance [7], [36] Hjorth1 (or VAR) -Interquartile Range [7], [26] IQR [26] Kurt -Log Detector [27], [34] LD -Standard Deviation Value [5], [7] SD -Skewness [7], [18] Skew -Linear Prediction Coefficient 2 [16], [37] 2nd LPC -Linear Prediction Coefficient 3 [16], [37] 3rd LPC -Zero Crossing [7], [38] ZC threshold : 0.03 × σrest Slope Sign Change [7], [38] SSC threshold : 0.03 × σrest Spectral Entropy [26] SpEn -Simple Square Integral [27], [33] SSI -Waveform Length [18], [27], [28] WL -Auto-Regressive Coefficient 1 [39], [40] AR1 order : 4 Auto-Regressive Coefficient 2 [39], [40] AR2 order : 4 Auto-Regressive Coefficient 3 [39], [40] AR3 order : 4 Auto-Regressive Coefficient 4 [39], [40] AR4 order : 4 Maximum-to-M...…”

Section: Methodsmentioning

confidence: 99%

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Meng

Jiang

Qin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upperlimb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS

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“…Out of the resulting IMF functions, the use of one of them [18] and four of them [19] will help with further analysis of the signal features.…”

Section: Semg Signal Analysismentioning

confidence: 99%

“…The use of feature extraction of the signal of each IMF components including the following is to be discussed [18]:…”

Section: Semg Signal Analysismentioning

confidence: 99%

General Concept of the EMG Controlled Bionic Hand

Pieprzycki

Król

2020

Sci. Tech., Innov

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Forearm movements classification of EMG signals using Hilbert Huang transform and artificial neural networks

Cited by 18 publications

References 0 publications

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

General Concept of the EMG Controlled Bionic Hand

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