Effect of Window Conditioning Parameters on the Classification Performance and Stability of EMG-Based Feature Extraction Methods

Asogbon, Mojisola Grace; Samuel, Oluwarotimi Williams; Geng, Yanjuan; Chen, Shixiong; Mzurikwao, Deogratias; Peng, Fang; Li, Guanglin

doi:10.1109/cbs.2018.8612246

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…[ 3 ] Root‐mean‐square (RMS) envelope of the filtered signal was calculated with a 200 ms wide moving window. [ 14,72 ]…”

Section: Resultsmentioning

confidence: 99%

“…[3] Root-mean-square (RMS) envelope of the filtered signal was calculated with a 200 ms wide moving window. [14,72] By controlling for using the same metal electrode, same skin location and treatment, we directly compared between electrolytic gel-skin interfaces and conducting polymer gel-skin interfaces. Recording snippets from three subjects along with the signal-to-noise ratio from the entire recordings are shown in Figure 3c.…”

Section: Baseline In Vivo Electromyographymentioning

confidence: 99%

See 1 more Smart Citation

Pure Conducting Polymer Hydrogels Increase Signal‐to‐Noise of Cutaneous Electrodes by Lowering Skin Interface Impedance

Martinez

Floch

Liu

et al. 2023

Adv Healthcare Materials

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Cutaneous electrodes are routinely used for noninvasive electrophysiological sensing of signals from the brain, the heart, and the neuromuscular system. These bioelectronic signals propagate as ionic charge from their sources to the skin–electrode interface where they are then sensed as electronic charge by the instrumentation. However, these signals suffer from low signal‐to‐noise ratio arising from the high impedance at the tissue‐to‐electrode contact interface. This paper reports that soft conductive polymer hydrogels made purely of poly(3,4‐ethylenedioxy‐thiophene) doped with poly(styrene sulfonate) present nearly an order of magnitude decrease in the skin–electrode contact impedance (88%, 82%, and 77% at 10, 100, and 1 kHz, respectively) when compared to clinical electrodes in an ex vivo model that isolates the bioelectrochemical features of a single skin–electrode contact. Integrating these pure soft conductive polymer blocks into an adhesive wearable sensor enables high fidelity bioelectronic signals with higher signal‐to‐noise ratio (average 2.1 dB increase, max 3.4 dB increase) when compared to clinical electrodes across all subjects. The utility of these electrodes is demonstrated in a neural interface application. The conductive polymer hydrogels enable electromyogram‐based velocity control of a robotic arm to complete a pick and place task. This work provides a basis for the characterization and use of conductive polymer hydrogels to better couple human and machine.

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“…[ 3 ] Root‐mean‐square (RMS) envelope of the filtered signal was calculated with a 200 ms wide moving window. [ 14,72 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Baseline In Vivo Electromyographymentioning

confidence: 99%

Pure Conducting Polymer Hydrogels Increase Signal‐to‐Noise of Cutaneous Electrodes by Lowering Skin Interface Impedance

Martinez

Floch

Liu

et al. 2023

Adv Healthcare Materials

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show abstract

“…In similar trends with other results, the multi-features outperformed the single features when 2-channels, 4-channels and 6-channels were considered. Finally, by critically analyzing of our results, we discovered that when classification error, computation time, and number of electrodes were considered together, most feature sets achieved good classification performance with optimal windowing parameters of 250 ms/100 ms. Also, discoveries from this study through the systematic approach adopted can facilitate positive development in other areas where optimal features and machine learning driven approaches are required [41][42][43][44][45][46][47][48][49][50]. Last, one limitation of the current work is that the EMG pattern recognition system for movement intent decoding was analyzed in an off-line mode, and we hope to conduct online and real-time analysis in our future work.…”

Section: Discussionmentioning

confidence: 99%

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

et al. 2020

Self Cite

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The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

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“…In segmentation, the overlapped segmentation was applied in some works, but its variation was not evaluated. Even knowing that this parameter (together with window length) affects the pattern recognition process [17,40], the effect of overlapping the segments on accuracy was not explored and was usually based on a fixed length of overlapped segments. During the classification, the contribution for each feature or the use of specific feature sets and their influence on accuracy were not evaluated.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Mendes

Freitas

Campos

et al. 2020

Sensors

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Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

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Effect of Window Conditioning Parameters on the Classification Performance and Stability of EMG-Based Feature Extraction Methods

Cited by 7 publications

References 19 publications

Pure Conducting Polymer Hydrogels Increase Signal‐to‐Noise of Cutaneous Electrodes by Lowering Skin Interface Impedance

Pure Conducting Polymer Hydrogels Increase Signal‐to‐Noise of Cutaneous Electrodes by Lowering Skin Interface Impedance

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

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