Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Úbeda, Andrés; Vecchio, Alessandro Del; Sartori, Massimo; Méndez, Santiago Timoteo Puente; Torres, Fernando; Azorín, José M.; Farina, Dario

doi:10.1109/icorr.2017.8009223

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Unlike a previous study which observed that, for MVC levels lower than 10%, the EMD was longer when the MVC level was lower [ 24 ], the current study did not observe such an effect. It still remains unknown whether, at lower levels of MVCs, the ultrasound-detected onset would be dependent on MVC levels.…”

Section: Discussioncontrasting

confidence: 99%

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Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

Ling

Zong-Hao

Shea

et al. 2020

Sensors

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Background: Available methods for studying muscle dynamics, including electromyography (EMG), mechanomyography (MMG) and M-mode ultrasound, have limitations in terms of spatial resolution. Methods: This study developed a novel method/protocol of two-dimensional mapping of muscle motion onset using ultrafast ultrasound imaging, i.e., sono-mechano-myo-graphy (SMMG). The developed method was compared with the EMG, MMG and force outputs of tibialis anterior (TA) muscle during ankle dorsiflexion at different percentages of maximum voluntary contraction (MVC) force in healthy young adults. Results: Significant differences between all pairwise comparisons of onsets were identified, except between SMMG and MMG. The EMG onset significantly led SMMG, MMG and force onsets by 40.0 ± 1.7 ms (p < 0.001), 43.1 ± 5.2 ms (p < 0.005) and 73.0 ± 4.5 ms (p < 0.001), respectively. Muscle motion also started earlier at the middle aponeurosis than skin surface and deeper regions when viewed longitudinally (p < 0.001). No significant effect of force level on onset delay was found. Conclusions: This study introduced and evaluated a new method/protocol, SMMG, for studying muscle dynamics and demonstrated its feasibility for muscle contraction onset research. This novel technology can potentially provide new insights for future studies of neuromuscular diseases, such as multiple sclerosis and muscular dystrophy.

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Section: Discussioncontrasting

confidence: 99%

“…This study detected a mean EMD of 73 ms, which is around half of that measured by Ubeda et al at 10% MVC in the same muscle [ 24 ]. This difference could be partly caused by the difference in positioning of the electrode, which can lead to 40–60 ms of error in EMD [ 25 ].…”

Section: Discussionmentioning

confidence: 54%

Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

Ling

Zong-Hao

Shea

et al. 2020

Sensors

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“…However, how the microstructures in the muscle combine to do so is less clear-the complexity and degree of linearity of the system are unknown. A representative example of a mechanism relevant for understanding the neuromechanics of muscle control is excitation-contraction coupling (E-C coupling), which is one of the components of the electromechanical delay (EMD)-a metric widely used to study human physiology (Cè et al 2014, Conchola et al 2015, Esposito et al 2016, Smith et al 2017, Úbeda et al 2017. E-C coupling refers to the communication between the electrical activity in the skeletal muscle fibre membrane and the release of calcium, eliciting the contraction (Calderón et al 2014).…”

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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“…Previous studies have shown that electromechanical delay (EMD), which is typically defined as the time lag between electrical activation of a muscle and the onset of the exerted force (Cavanagh and Komi, 1979), is between 30 and 150 ms (Zhou et al, 1995; Blackburn et al, 2009; Nordez et al, 2009; Yavuz et al, 2010). Úbeda et al (2017) even found EMDs ranging from 112 to 361 ms. Considering that EMG appeared about 125 ms before force generation (Blackburn et al, 2009), the 170 ms time lag in our training system (which was caused by both the filter and the exoskeleton) is very short and acceptable.…”

Section: Discussionmentioning

confidence: 98%

Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context

et al. 2019

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As a leading cause of loss of functional movement, stroke often makes it difficult for patients to walk. Interventions to aid motor recovery in stroke patients should be carried out as a matter of urgency. However, muscle activity in the knee is usually too weak to generate overt movements, which poses a challenge for early post-stroke rehabilitation training. Although electromyography (EMG)-controlled exoskeletons have the potential to solve this problem, most existing robotic devices in rehabilitation centers are expensive, technologically complex, and allow only low training intensity. To address these problems, we have developed an EMG-controlled knee exoskeleton for use at home to assist stroke patients in their rehabilitation. EMG signals of the subject are acquired by an easy-to-don EMG sensor and then processed by a Kalman filter to control the exoskeleton autonomously. A newly-designed game is introduced to improve rehabilitation by encouraging patients' involvement in the training process. Six healthy subjects took part in an initial test of this new training tool. The test showed that subjects could use their EMG signals to control the exoskeleton to assist them in playing the game. Subjects found the rehabilitation process interesting, and they improved their control performance through 20-block training, with game scores increasing from 41.3 ± 15.19 to 78.5 ± 25.2. The setup process was simplified compared to traditional studies and took only 72 s according to test on one healthy subject. The time lag of EMG signal processing, which is an important aspect for real-time control, was significantly reduced to about 64 ms by employing a Kalman filter, while the delay caused by the exoskeleton was about 110 ms. This easy-to-use rehabilitation tool has a greatly simplified training process and allows patients to undergo rehabilitation in a home environment without the need for a therapist to be present. It has the potential to improve the intensity of rehabilitation and the outcomes for stroke patients in the initial phase of rehabilitation.

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Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

Cited by 7 publications

References 16 publications

Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

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

Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context

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