Identifying intrinsic and reflexive contributions to low-back stabilization

Drunen, Paul van; Maaswinkel, E.; Helm, F.C.T. van der; Dieën, Jaap H. van; Happee, Riender

doi:10.1016/j.jbiomech.2013.03.007

Cited by 43 publications

(85 citation statements)

References 28 publications

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“…a shorter delay was estimated due to the proximity to the spinal cord, matching the 18 ms delay reported for the cervicocollic reflex in cats [21]. For the lumbar controller, T de was 25 ms, which is relatively close to the 30 ms that has been reported for the lumbar spine muscles [23]. The control signal, u(t), is converted to a muscle activation request by scaling with the maximum isometric strength of each controlled muscle group.…”

Section: Musculoskeletal Feedback Control Modelsupporting

confidence: 71%

A method to model anticipatory postural control in driver braking events

et al. 2014

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Human body models (HBMs) for vehicle occupant simulations have recently been extended with active muscles and postural control strategies. Feedback control has been used to model occupant responses to autonomous braking interventions. However, driver postural responses during driver initiated braking differ greatly from autonomous braking. In the present study, an anticipatory postural response was hypothesized, modelled in a whole-body HBM with feedback controlled muscles, and validated using existing volunteer data. The anticipatory response was modelled as a time dependent change in the reference value for the feedback controllers, which generates correcting moments to counteract the braking deceleration. The results showed that, in 11 m/s(2) driver braking simulations, including the anticipatory postural response reduced the peak forward displacement of the head by 100mm, of the shoulder by 30 mm, while the peak head flexion rotation was reduced by 18°. The HBM kinematic response was within a one standard deviation corridor of corresponding test data from volunteers performing maximum braking. It was concluded that the hypothesized anticipatory responses can be modelled by changing the reference positions of the individual joint feedback controllers that regulate muscle activation levels. The addition of anticipatory postural control muscle activations appears to explain the difference in occupant kinematics between driver and autonomous braking. This method of modelling postural reactions can be applied to the simulation of other driver voluntary actions, such as emergency avoidance by steering.

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Section: Musculoskeletal Feedback Control Modelsupporting

confidence: 71%

A method to model anticipatory postural control in driver braking events

et al. 2014

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“…Due to the high number of recorded periods and subsequent low noise level in this study, the VAF was high compared to other studies were the mechanical admittance was modelled from much less recorded periods. In previous studies on the wrist and other joints [8,19,25,29] a VAF between 80% and 95% was obtained when using a parametric linear model to describe the relation between angle and torque. The averaged VAF obtained when modelling the EMG signal was around 70% for both muscles, which is slightly higher than the same studies mentioned before where a VAF of 40% to 60% was common when modelling the EMG signals with a parametric linear model.…”

Section: Fitting the Best Nonparametric Linear Modelmentioning

confidence: 99%

Quantifying Nonlinear Contributions to Cortical Responses Evoked by Continuous Wrist Manipulation

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Solis‐Escalante

Vardy

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Cortical responses to continuous stimuli as recorded using either magneto-or electroencephalography (EEG) have shown power at harmonics of the stimulated frequency, indicating nonlinear behavior. Even though the selection of analysis techniques depends on the linearity of the system under study, the importance of nonlinear contributions to cortical responses has not been formally addressed. The goal of this paper is to quantify the nonlinear contributions to the cortical response obtained from continuous sensory stimulation. EEG was used to record the cortical response evoked by continuous movement of the wrist joint of healthy subjects applied with a robotic manipulator. Multisine stimulus signals (i.e. the sum of several sinusoids) elicit a periodic cortical response and allow to assess the nonlinear contributions to the response. Wrist dynamics (relation between joint angle and torque) were successfully linearized, explaining 99% of the response. In contrast, the cortical response revealed a highly nonlinear relation; where most power (~80%) occurred at nonstimulated frequencies. Moreover, only 10% of the response could be explained using a nonparametric linear model. These results indicate that the recorded evoked cortical responses are governed by nonlinearities and that linear methods do not suffice when describing the relation between mechanical stimulus and cortical response.

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“…Intrinsic low-back stiffness and damping, comprising passive tissue and passive muscle properties, can be altered by muscle cocontraction, while reflexes include proprioceptive (muscle spindle), vestibular, and visual feedback (Day et al 1997;Goodworth andPeterka 2009, 2010;van Drunen et al 2013van Drunen et al , 2015. The function of intrinsic properties and muscle spindle feedback is to minimize lumbar bending and thus to contribute to stabilizing the trunk with respect to the pelvis (e.g., in power lifting), which we define as trunk-on-pelvis stabilization.…”

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confidence: 99%

“…Low-back motor control modulation has been found for changing task instructions, perturbation amplitude and frequency bandwidth, and the availability of feedback such as vision or vestibular information (Buchanan and Horak 1999;Goodworth andPeterka 2009, 2010;van Drunen et al 2013van Drunen et al , 2015. This study focused on modulation due to task instruction, vision, and perturbation bandwidth.…”

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confidence: 99%