Advances in EMG measurement techniques, analysis procedures, and the impact of muscle mechanics on future requirements for the methodology

Avila, Elisa Romero; Williams, S.; Disselhorst‐Klug, Catherine

doi:10.1016/j.jbiomech.2023.111687

Cited by 2 publications

(2 citation statements)

References 134 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used a single differential electrode configuration with two electrodes (10 mm × 1 mm, with an interelectrode distance of 10 mm), which suited our purpose of demonstrating the usefulness of the model as a tool to better understand the relationship between underlying mechanisms of SCI and the sEMG signal. With modifications, the configuration can be expanded from two electrodes to linear electrode arrays or two-dimensional HDsEMG configurations, which have gained popularity to obtain spatial distribution of muscle activations [21,62,63]. Simulating HDsEMG is an exciting future avenue that may yield information about the alterations in spatial patterns of bioelectric activity in muscles after SCI, thereby complementing the temporal signal properties emphasized in the present study.…”

Section: Discussionmentioning

confidence: 95%

“…Recent reviews by Balbinot et al demonstrated that sEMG features in time and frequency domains, other than traditional amplitude-based features, have not been fully leveraged [19,20]. Nonetheless, the use of sEMG in the clinical settings has been limited, with one of the key barriers being the specialized training and expertise required for signal interpretation [18,21]. Therefore, studying the mechanisms of SCI disruptions on the sEMG signal is crucial for a deeper understanding and more accurate interpretation of the signal, leading to further usage of the technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A computational model of surface electromyography signal alterations after spinal cord injury

Li,

Balbinot,

Furlan

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to study in vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. After simulating sEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on sEMG features using the Kendall Rank Correlation analysis. The results indicate that commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.

show abstract

Section: Discussionmentioning

confidence: 95%