Improved Assessment of Muscle Excitation From Surface Electromyograms in Isometric Muscle Contractions

Kranjec, Jernej; Holobar, Aleš

doi:10.1109/tnsre.2019.2922453

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Therefore, the impact of MUAPs needs to be removed from muscle excitation estimation. In this study, we tested two different MUAP removal techniques, namely full HDEMG decomposition to contributions of individual MUs and CAI technique proposed in [10]. Full HDEMG decomposition supports exact control of the quality, removes all impacts of noise and MUAPs from muscle estimation, but is also highly selective, as only reliably identified MUs are considered.…”

Section: Discussionmentioning

confidence: 99%

“…In the same experimental setup, CKC-based MU identification required 19.54 ± 8.37 s per one second of HDEMG signals. Detailed analysis of the computational complexity of CST, CAI and RMS metrics is provided in [10].…”

Section: Discussionmentioning

confidence: 99%

“…By summing up K consecutive AI values, we get the Cumulative AI (CAI), which cancels out the MUAP shapes and reveals the cumulative MU spike train [10]:…”

Section: Cumulative Activity Indexmentioning

confidence: 99%

“…Recently, Cumulative Motor Unit Activity Index (CAI) methodology has been introduced [10], supporting the realtime MUAP compensation in EMG. In this study, we evaluated its potential to estimate the muscle co-activations in one DOF movements.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes

Potočnik

Divjak

Urh

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density Elec-troMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 % dispersion around the real value), followed by the CST (7 % dispersion) and CAI (5 % dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…By summing up K consecutive AI values, we get the Cumulative AI (CAI), which cancels out the MUAP shapes and reveals the cumulative MU spike train [10]:…”

Section: Cumulative Activity Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes

Potočnik

Divjak

Urh

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…Different approaches to muscle activation estimation from MU discharge patterns were also developed, including Principal Component Analysis of smoothed MU discharge rates (Negro et al, 2009), NMF of smoothed MU discharge rates (Tanzarella et al, 2021) and Cumulative Spike Train (CST) of identified MUs (Farina et al, 2014). Indeed, studies of simultaneous MU behavior in different muscles are increasing (Héroux et al, 2014;de Souza et al, 2018;Kranjec and Holobar, 2019;Davis et al, 2020;Potočnik et al, 2020;Cohen et al, 2021;Tanzarella et al, 2021). However, to the best of our knowledge, comparisons of MU behavior in a group of simultaneously recorded skeletal muscles of older and young persons are still largely lacking.…”

Section: Introductionmentioning

confidence: 99%

Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

et al. 2022

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We examined activation patterns of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL), soleus (SO), and tibialis anterior (TA) muscles in eight older (58.4 ± 3.3 years) and seven young (23.1 ± 2.9 years) participants, before and after 14 days of horizontal bed rest. Visual feedback on the exerted muscle torque was provided to the participants. The discharge patterns of individual motor units (MUs) were studied in three repetitions of isometric plantar flexion at 30 and 60% of Maximum Voluntary Contraction (MVC), before, and 1 day after the 14-day bed rest, respectively. In the GL and GM muscles, the older participants demonstrated higher MU discharge rates than the young, regardless of the contraction level, both before and after the bed rest. In the TA and SO muscles, the differences between the older and young participants were less consistent. Detailed analysis revealed person-specific changes in the MU discharge rates after the bed rest. To quantify the coactivation patterns we calculated the correlation coefficients between the cumulative spike trains of identified MUs from each muscle, and measured the root mean square difference of the correlation coefficients between the trials of the same session (intra-session variability) and between different sessions (inter-session variability) in each participant (intra-person comparison) and across participants (inter-person comparison). In the intra-person comparison, the inter-session variability was higher than the intra-session variability, either before or after the bed rest. At 60% MVC torque, the young demonstrated higher inter-person variability of coactivation than the older participants, but this variability decreased significantly after the bed rest. In older participants, inter-person variability was consistently lower at 60% than at 30% MVC torque. In young participants, inter-person variability became lower at 60% than at 30% MVC torque only after the bed rest. Precaution is required when analyzing the MU discharge and coactivation patterns, as individual persons demonstrate individual adaptations to aging or bed rest.

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CG-Recognizer: A biosignal-based continuous gesture recognition system

Zheng

Wang²,

Deng³

et al. 2022

Biomedical Signal Processing and Control

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Improved Assessment of Muscle Excitation From Surface Electromyograms in Isometric Muscle Contractions

Cited by 7 publications

References 20 publications

Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes

Estimation of Muscle Co-Activations in Wrist Rehabilitation After Stroke is Sensitive to Motor Unit Distribution and Action Potential Shapes

Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

CG-Recognizer: A biosignal-based continuous gesture recognition system

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