Differences in muscle contraction onset as determined by ultrasound and electromyography

Tweedell, Andrew J.; Tenan, Matthew S.; Haynes, Courtney

doi:10.1002/mus.26395

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Both iEMG and sEMG allow the indirect identification of MN discharges by the analysis of the corresponding muscle unit APs. On the other hand, the mechanical response of entire muscles can be studied using ultrasound (US) (Botter et al 2013, Dieterich et al 2017, Tweedell et al 2019. US has also been used for studying the mechanical response of individual MUs in electrically stimulated contractions (Deffieux et al 2008, Waasdorp et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

et al. 2022

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Objective: The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e., the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions. Approach: We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay. Main results: Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9 ± 5.3 ms, 56.6 ± 8.4 ms, and 3.8 ± 3.0 ms (n = 390 across 10 participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions. Significance: The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.

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Section: Introductionmentioning

confidence: 99%

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

et al. 2022

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“…Both iEMG and sEMG allow the indirect identification of MN discharges by the analysis of the corresponding muscle unit APs. On the other hand, the mechanical response of entire muscles can be studied using ultrasound (US) (Botter et al, 2013;Dieterich et al, 2017;Tweedell et al, 2019). Whilst US has also been used for studying the mechanical response of individual MUs, these approaches are limited to electrically stimulated contractions (Deffieux et al, 2008;Waasdorp et al, 2019Waasdorp et al, , 2021, or by their reliance on invasive EMG techniques (Rohlén et al, 2020b(Rohlén et al, , 2020a.…”

Section: Introductionmentioning

confidence: 99%

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

Lubel

Sgambato

Barsakcioglu

et al. 2022

Preprint

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The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, the muscle deformation, has not been widely studied. Here, we describe a methodology utilising ultrafast ultrasound (US), allowing imaging of up to tens of thousands of frames per second, to measure the deformation of the muscular tissue due to individual MU twitches for a population of active MUs during voluntary contractions. We used the spiking activity of MUs decomposed from high-density surface electromyography recordings of the tibialis anterior muscle to guide the analysis of simultaneously recorded ultrafast US. With a novel analysis on the US images we identified, with high spatio-temporal precision, the velocity maps associated with single MU movements. From the individual MU profiles obtained from the velocity maps, the region of movement, the duration of the mechanical twitch, the total and active contraction times, and the activation time were computed. The latter features, the temporal features, showed high repeatability across different force levels. The former feature, the spatial feature, showed high consistency across force levels, however the complicated dynamics of the muscle motion resulted in morphing and translation of these regions. Furthermore, the experimental measures provided the first evidence of muscle unit twisting during voluntary contractions. The proposed approach allows, for the first time, non-invasive recordings of muscle deformation due to individual MU activations during voluntary contractions.Key pointsWe identified the activity of single motor units (MUs) from high-density surface electromyography (HDsEMG) and used this information in combination with ultrafast ultrasound to extract local muscle motion due to the contraction of individual muscle unitsMultiple MUs, including those with fibres overlapping in space, can be simultaneously and individually detected using this techniqueThe proposed method allows us to measure both the spiking activity of motor units and their movement within the muscles concomitantlyThe technique allows for populations of MUs to be tracked and monitored in the electrical and mechanical domains simultaneously and non-invasively during natural contractions, thus achieving a high spatio-temporal resolution in the characterization of MU behaviour

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“…For example, muscle activity measured by electromyography (EMG) [35] or sonomyography (SMG) [36] may assist with identifying the segmentation point between reaching and targeting when combined with kinematics. While these modalities have been used in other contexts such as detecting grasp intention (e.g., [38] ) or movement onset [37] , it is unclear if they could be used to identify the targeting primitive. If so, this approach could be used to help create ground truth data for developing and evaluating segmentation approaches that use kinematics alone.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

Jackson

Duric

Engdahl

et al. 2024

IEEE J. Transl. Eng. Health Med.

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There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

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Differences in muscle contraction onset as determined by ultrasound and electromyography

Cited by 14 publications

References 44 publications

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

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