Identification of the human postural control system through stochastic trajectory optimization

Wang, Huawei; Bogert, Antonie J. van den

doi:10.1016/j.jneumeth.2020.108580

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…This dataset contains motion, ground reaction force, IMU, and EMG data from normal and perturbed gait data, where perturbations were generated at controlled times during randomly selected gait cycles. The EMG and motion data for the unperturbed gait are consistent with what has been seen in previous studies on normal gait ( Winter & Yack, 1987 ; Wang & van den Bogert, 2020 ). The EMG data for the perturbed gait shows evidence that a reflex occurs ( Fig.…”

Section: Discussionsupporting

confidence: 89%

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

Lorenz,

van den Bogert

2024

PeerJ

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Background Humans have a remarkable capability to maintain balance while walking. There is, however, a lack of publicly available research data on reactive responses to destabilizing perturbations during gait. Methods Here, we share a comprehensive dataset collected from 10 participants who experienced random perturbations while walking on an instrumented treadmill. Each participant performed six 5-min walking trials at a rate of 1.2 m/s, during which rapid belt speed perturbations could occur during the participant’s stance phase. Each gait cycle had a 17% probability of being perturbed. The perturbations consisted of an increase of belt speed by 0.75 m/s, delivered with equal probability at 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the stance phase. Data were recorded using motion capture with 25 markers, eight inertial measurement units (IMUs), and electromyography (EMG) from the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (LG), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GM). The full protocol is described in detail. Results We provide marker trajectories, force plate data, EMG data, and belt speed information for all trials and participants. IMU data is provided for most participants. This data can be useful for identifying neural feedback control in human gait, biologically inspired control systems for robots, and the development of clinical applications.

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Section: Discussionsupporting

confidence: 89%

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

Lorenz,

van den Bogert

2024

PeerJ

Self Cite

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“…The perturbation signal was taken from Wang and van den Bogert ( 2020a ), where a 5 min perturbation signal was applied to human participants. This signal was designed using random square pulses with different amplitudes, [−5, −2.5, 0, 2.5, 5] cm, and different pulse durations, [0.25, 0.5, 0.75, 1.0, 1.25, 1.5] s (Wang and van den Bogert, 2020a ). However, the optimization platform limited the simulations to be around 100 s due to technical limitations, so only part of this signal was used.…”

Section: Methodsmentioning

confidence: 99%

“…However, the optimization platform limited the simulations to be around 100 s due to technical limitations, so only part of this signal was used. The platform that was modeled in Webots was controlled to move exactly as the platform motion recorded in the experiment (Wang and van den Bogert, 2020a ). The simulation modeled the perturbation signal starting at a randomly chosen start time, 53.73 s.…”

Section: Methodsmentioning

confidence: 99%

“…A control system should be studied via an indirect approach, with perturbations, to identify the sensitivity of the controller and plant to noise, because a direct approach incorrectly assumes the control and state to be independent and is only appropriate for open-loop systems (Van der Kooij et al, 2005 ). Balance control has been identified through perturbed standing data for different scientific, robotic, and clinical applications (Wang and van den Bogert, 2020a ), e.g., to provide insight into the different sensors used to balance, or to implement the identified controller into a robot.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Contribution of Proprioceptive Reflexes to Balance Control in Perturbed Standing

Koelewijn

Ijspeert

2020

Front. Bioeng. Biotechnol.

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Humans control balance using different feedback loops involving the vestibular system, the visual system, and proprioception. In this article, we focus on proprioception and explore the contribution of reflexes based on force and length feedback to standing balance. In particular, we address the questions of how much proprioception alone could explain balance control, and whether one modality, force or length feedback, is more important than the other. A sagittal plane neuro-musculoskeletal model was developed with six degrees of freedom and nine muscles in each leg. A controller was designed using proprioceptive reflexes and a dead zone. No feedback control was applied inside the dead zone. Reflexes were active once the center of mass moved outside the dead zone. Controller parameters were found by solving an optimization problem, where effort was minimized while the neuro-musculoskeletal model should remain standing upright on a perturbed platform. The ground was perturbed with random square pulses in the sagittal plane with different amplitudes and durations. The optimization was solved for three controllers: using force and length feedback (base model), using only force feedback, and using only length feedback. Simulations were compared to human data from previous work, where an experiment with the same perturbation signal was performed. The optimized controller yielded a similar posture, since average joint angles were within 5 degrees of the experimental average joint angles. The joint angles of the base model, the length only model, and the force only model correlated weakly (ankle) to moderately with the experimental joint angles. The ankle moment correlated weakly to moderately with the experimental ankle moment, while the hip and knee moment were only weakly correlated, or not at all. The time series of the joint angles showed that the length feedback model was better able to explain the experimental joint angles than the force feedback model. Changes in time delay affected the correlation of the joint angles and joint moments. The objective of effort minimization yielded lower joint moments than in the experiment, suggesting that other objectives are also important in balance control, which cause an increase in effort and thus larger joint moments.

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Identification of Postural Controllers in Human Standing Balance

Wang

Bogert

2020

Journal of Biomechanical Engineering

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Standing balance is a simple motion task for healthy humans but the actions of the central nervous system (CNS) have not been described by generalized and sufficiently sophisticated control laws. While system identification approaches have been used to extracted models of the CNS, they either focus on short balance motions, leading to task-specific control laws, or assume that the standing balance system is linear. To obtain comprehensive control laws for human standing balance, complex balance motions, long duration tests, and nonlinear controller models are all needed. In this paper, we demonstrate that trajectory optimization with the direct collocation method can achieve these goals to identify complex CNS models for the human standing balance task. We first examined this identification method using synthetic motion data and showed that correct control parameters can be extracted. Then, six types of controllers, from simple linear to complex nonlinear, were identified from 100 seconds of motion data from randomly perturbed standing. Results showed that multiple time-delay paths and nonlinear properties are both needed in order to fully explain human feedback control of standing balance.

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Identification of the human postural control system through stochastic trajectory optimization

Cited by 5 publications

References 17 publications

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations

Exploring the Contribution of Proprioceptive Reflexes to Balance Control in Perturbed Standing

Identification of Postural Controllers in Human Standing Balance

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