Assessment of the underlying systems involved in standing balance: the additional value of electromyography in system identification and parameter estimation

Journal of NeuroEngineering and Rehabilitation, Sep 2017

Closed loop system identification (CLSIT) is a method to disentangle the contribution of underlying systems in standing balance. We investigated whether taking into account lower leg muscle activation in CLSIT could improve the reliability and accuracy of estimated parameters identifying the underlying systems. Standing balance behaviour of 20 healthy young participants was measured using continuous rotations of the support surface (SS). The dynamic balance behaviour obtained with CLSIT was expressed by sensitivity functions of the ankle torque, body sway and muscle activation of the lower legs to the SS rotation. Balance control models, 1) without activation dynamics, 2) with activation dynamics and 3) with activation dynamics and acceleration feedback, were fitted on the data of all possible combinations of the 3 sensitivity functions. The reliability of the estimated model parameters was represented by the mean relative standard errors of the mean (mSEM) of the estimated parameters, expressed for the basic parameters, the activation dynamics parameters and the acceleration feedback parameter. To investigate the accuracy, a model validation study was performed using simulated data obtained with a comprehensive balance control model. The accuracy of the estimated model parameters was described by the mean relative difference (mDIFF) between the estimated parameters and original parameters. The experimental data showed a low mSEM of the basic parameters, activation dynamics parameters and acceleration feedback parameter by adding muscle activation in combination with activation dynamics and acceleration feedback to the fitted model. From the simulated data, the mDIFF of the basic parameters varied from 22.2–22.4% when estimated using the torque and body sway sensitivity functions. Adding the activation dynamics, acceleration feedback and muscle activation improved mDIFF to 13.1–15.1%. Adding the muscle activation in combination with the activation dynamics and acceleration feedback to CLSIT improves the accuracy and reliability of the estimated parameters and gives the possibility to separate the neural time delay, electromechanical delay and the intrinsic and reflexive dynamics. To diagnose impaired balance more specifically, it is recommended to add electromyography (EMG) to body sway (with or without torque) measurements in the assessment of the underlying systems.

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Assessment of the underlying systems involved in standing balance: the additional value of electromyography in system identification and parameter estimation

Pasma et al. Journal of NeuroEngineering and Rehabilitation Assessment of the underlying systems involved in standing balance: the additional value of electromyography in system identification and parameter estimation J. H. Pasma 0 J. van Kordelaar 2 D. de Kam 1 V. Weerdesteyn 1 3 A. C. Schouten 0 2 H. van der Kooij 0 2 0 Department of Biomechanical Engineering, Delft University of Technology , Mekelweg 2, 2628 CD Delft , The Netherlands 1 Department of Rehabilitation, Donders Institute for Brain , Cognition and Behaviour , Radboud University Medical Center , Nijmegen , The Netherlands 2 Department of Biomechanical Engineering, Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente , Enschede , The Netherlands 3 Sint Maartenskliniek Research , Nijmegen , The Netherlands Background: Closed loop system identification (CLSIT) is a method to disentangle the contribution of underlying systems in standing balance. We investigated whether taking into account lower leg muscle activation in CLSIT could improve the reliability and accuracy of estimated parameters identifying the underlying systems. Methods: Standing balance behaviour of 20 healthy young participants was measured using continuous rotations of the support surface (SS). The dynamic balance behaviour obtained with CLSIT was expressed by sensitivity functions of the ankle torque, body sway and muscle activation of the lower legs to the SS rotation. Balance control models, 1) without activation dynamics, 2) with activation dynamics and 3) with activation dynamics and acceleration feedback, were fitted on the data of all possible combinations of the 3 sensitivity functions. The reliability of the estimated model parameters was represented by the mean relative standard errors of the mean (mSEM) of the estimated parameters, expressed for the basic parameters, the activation dynamics parameters and the acceleration feedback parameter. To investigate the accuracy, a model validation study was performed using simulated data obtained with a comprehensive balance control model. The accuracy of the estimated model parameters was described by the mean relative difference (mDIFF) between the estimated parameters and original parameters. Results: The experimental data showed a low mSEM of the basic parameters, activation dynamics parameters and acceleration feedback parameter by adding muscle activation in combination with activation dynamics and acceleration feedback to the fitted model. From the simulated data, the mDIFF of the basic parameters varied from 22.2-22.4% when estimated using the torque and body sway sensitivity functions. Adding the activation dynamics, acceleration feedback and muscle activation improved mDIFF to 13.1-15.1%. Conclusions: Adding the muscle activation in combination with the activation dynamics and acceleration feedback to CLSIT improves the accuracy and reliability of the estimated parameters and gives the possibility to separate the neural time delay, electromechanical delay and the intrinsic and reflexive dynamics. To diagnose impaired balance more specifically, it is recommended to add electromyography (EMG) to body sway (with or without torque) measurements in the assessment of the underlying systems. Posture; Human balance control; Modelling; Muscle activation; Activation dynamics Background Impaired balance is a common complaint in elderly and patients with specific diseases like vestibular disorders, stroke or Parkinson’s disease [ 1–6 ]. To maintain standing balance, several underlying systems interact, such as the nervous system, sensory systems and motor system. With age, specific diseases and medication use, these systems deteriorate and compensate for each other’s deteriorations [ 7–10 ], which makes it difficult to detect the underlying cause of impaired balance. To diagnose and intervene impaired balance with targeted interventions it is important to detect the underlying cause of impaired balance. Closed loop system identification (CLSIT) combined with perturbations is a method to distinguish the contribution of underlying systems in standing balance by taken into account the interrelation between the underlying systems and to describe the underlying systems with physiologically meaningful parameters. This gives the possibility to identify the underlying changes in standing balance and therefore to diagnose impaired balance more specifically [ 11, 12 ]. Several studies used CLSIT to assess standing balance in a variety of patient groups, such as elderly [ 13–16 ], vestibular loss patients [17], Parkinson’s disease patients [ 18–20 ] and stroke patients [ 21 ], by describing underlying changes in standing balance with physiologically meaningful parameters. To ‘open’ the closed loop, sensory and/or mechanical perturbations were applied to disentangle cause and effect and to assess the contribution of the sensory systems (i.e. proprioception, vision and vestibular system), (...truncated)


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J. H. Pasma, J. van Kordelaar, D. de Kam, V. Weerdesteyn, A. C. Schouten, H. van der Kooij. Assessment of the underlying systems involved in standing balance: the additional value of electromyography in system identification and parameter estimation, Journal of NeuroEngineering and Rehabilitation, 2017, pp. 97,