Single-trial estimation of quasi-static EMG-to-joint-mechanical-impedance relationship over a range of joint torques

Dai, Chenyun; Martel, Stéphane; Martel, F.; Rancourt, Denis; Clancy, Edward A.

doi:10.1016/j.jelekin.2019.02.001

Cited by 7 publications

(4 citation statements)

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“…In addition, it has been already demonstrated that neurological diseases lead to a significant deterioration of ankle impedance, with respect to healthy subjects [21]. The application of dynamic perturbations to the examined anatomical joint and successive analysis of torque vs. angle graphs is currently the most widespread methodology for the measurement of the dynamic joint mechanical impedance [20,[22][23][24]. Different studies have been proposed in the literature for the objective measurement of ankle impedance in adult subjects.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

et al. 2019

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A deep analysis of ankle mechanical properties is a fundamental step in the design of an exoskeleton, especially if it is to be suitable for both adults and children. This study aims at assessing age-related differences of ankle properties using pediAnklebot. To achieve this aim, we enrolled 16 young adults and 10 children in an experimental protocol that consisted of the evaluation of ankle mechanical impedance and kinematic performance. Ankle impedance was measured by imposing stochastic torque perturbations in dorsi-plantarflexion and inversion-eversion directions. Kinematic performance was assessed by asking participants to perform a goaldirected task. Magnitude and anisotropy of impedance were computed using a multipleinput multiple-output system. Kinematic performance was quantified by computing indices of accuracy, smoothness, and timing. Adults showed greater magnitude of ankle impedance in both directions and for all frequencies, while the anisotropy was higher in children. By analyzing kinematics, children performed movements with lower accuracy and higher smoothness, while no differences were found for the duration of the movement. In addition, adults showed a greater ability to stop the movement when hitting the target. These findings can be useful to a proper development of robotic devices, as well as for implementation of specific training programs.

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Section: Introductionmentioning

confidence: 99%

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

et al. 2019

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“…Estimates of the standard deviation of the surface electromyogram (EMG) signal (EMGσ) serve as a global measure of muscular activation [ 1 , 2 , 3 ]. EMGσ is used to estimate the torque [ 4 , 5 , 6 , 7 , 8 , 9 ] and mechanical impedance [ 10 , 11 , 12 , 13 , 14 , 15 ] of a joint in motor control research [ 16 ] and many applications [ 17 ] including prosthesis control [ 18 , 19 , 20 ], ergonomic assessment [ 21 , 22 ], and clinical biomechanics [ 23 , 24 ]. Advanced single-channel EMGσ estimates comprise a cascade of ( Figure 1 ) [ 25 ] a high-pass filter at 10–20 Hz (to remove DC offsets and attenuate motion artifacts; the precise cut-off selection is not critical over this range), notch filters (to reject power-line interference and its harmonics), a whitening filter (to temporally uncorrelate the samples), a detector (absolute value or square; when absolute value detection is used, the data stream should be multiplied by

for the proper scaling of the subsequent resting noise correction [ 26 ]), a low-pass filter ( f c ≤ a few Hz, DC gain = 1), a re-linearizer (only required if a square-law detector is used and would then consist of a square root operation), and resting noise correction.…”

Section: Introductionmentioning

confidence: 99%

“…However, this reduction in variation is considerable—depending on the low-pass filter cut-off frequency [ 30 ], whitening can increase the signal-to-noise ratio (SNR) of constant effort contractions by 32–64% [ 29 , 31 ]. Applications that use EMGσ benefit in turn [ 4 , 10 , 32 , 33 ]. Hence, it is important to find simple and robust methods to implement whitening.…”

Section: Introductionmentioning

confidence: 99%

Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

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et al. 2021

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To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.

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“…This idea differs from the relationship between EMG and joint torque because, unlike from the torque modulation, the impedance can be modulated by co-contracting the muscles surrounding the joint [106]. Recent work by Dai et al developed a model to relate EMG of the biceps and triceps muscles to the corresponding impedance of the elbow across a range of joint torques [106]. In addition, the ankle impedance and corresponding muscle contraction of the lower extremity muscles has been studied.…”

Section: Electromyography and Joint Dynamicsmentioning

confidence: 99%

Estimation of Multi-Directional Ankle Impedance as a Function of Lower Extremity Muscle Activation

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Single-trial estimation of quasi-static EMG-to-joint-mechanical-impedance relationship over a range of joint torques

Cited by 7 publications

References 57 publications

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

Estimation of Multi-Directional Ankle Impedance as a Function of Lower Extremity Muscle Activation

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