Balance Recovery Prediction with Multiple Strategies for Standing Humans

Aftab, Zohaib; Thomas, Robert; Wieber, Pierre-Brice

doi:10.1371/journal.pone.0151166

Cited by 25 publications

(31 citation statements)

References 36 publications

(61 reference statements)

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“…This comparison also shows that for larger disturbances, our model favors using more hip movement because ankle movement is not sufficient to maintain balance. In our simulations, the ankle and hip movements observed were different from the results of Aftab et al [27]- [30], where the upper body was not included. A screenshot of a simulation animation for a disturbing force of 120 [N ] is shown in Fig.…”

Section: A Kinematic and Dynamic Analysiscontrasting

confidence: 99%

“…Nenchev [26] studied the deciding between the ankle and hip strategies for balance recovery depending on acceleration data measured during the impact. Aftab et al [27]- [30] proposed a multistep balance recovery scheme based on linear model predictive control (LMPC) by minimizing the horizontal CoM velocity and angular velocity of a flywheel, including the use of a hip strategy and a variable-step duration to correct large perturbations on an inverted pendulum or an inverted pendulum plus a flywheel. Thus, the typical kinematics of the human hip strategy could not be observed.…”

Section: Introductionmentioning

confidence: 99%

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Human-like Balance Recovery Based on Numerical Model Predictive Control Strategy

2020

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The purpose of this study is to implement a human-like balance recovery controller and analyze its robustness and energy consumption. Three main techniques to maintain balance can be distinguished in humans, namely (i) the ankle strategy, (ii) the hip-ankle strategy, (iii) the stepping strategy. Because we only consider quiet standing balance, then stepping is not included in our balance recovery study. Numerical model predictive control (N-MPC) is proposed to predict the best way to maintain balance against various disturbance forces. To simulate balance recovery, we build a three-link model including a foot with unilateral constraints, the lower body, and the upper body. Subsequently, we derive the dynamical equations of the model and linearize them. Based on human balance capabilities, we set bound constraints on our model, including angles and balance torques of the ankle and hip. Unilateral constraints are set on the foot, which makes our model more similar to the human quiet standing case. Finally, we implemented a simulation of the proposed ankle and hip-ankle strategy in simulation and analyzed the obtained results from kinematic and dynamic indices as well as from an energy consumption perspective. The robustness of the proposed controller was verified through the obtained simulation results. Thus, this study provides a better understanding of human quiet standing balance that could be useful for rehabilitation. INDEX TERMS Balance recovery, hip-ankle strategy, numerical model predictive control, energy consumption.

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Section: A Kinematic and Dynamic Analysiscontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human-like Balance Recovery Based on Numerical Model Predictive Control Strategy

2020

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“…Alternatively, a controller-based approach can be taken to predict balance responses and to predict the occurrence of reactive stepping. A Model Predictive Control scheme was introduced to generate ankle, hip and stepping balance recovery strategies in simulations based on a linear inverted pendulum model with a flywheel segment [13,14]. By adjusting weights in a certain optimization criterion the different balance strategies were regulated.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters that are used for describing limits on balance without using a classification approach can be potential features for training a classification algorithm. In modelbased or data-driven approaches, a common choice is to use the CoM and CoM velocity to describe standing balance [4,5,8,13,14,21,22]. Furthermore, the CoP and CoP velocity were used as stability margins [23][24][25] and the trunk angular velocity was used as a controller input for predicting balance responses [13,14].…”

Section: Introductionmentioning

confidence: 99%

Predicting reactive stepping in response to perturbations by using a classification approach

Emmens

Asseldonk

Prinsen

et al. 2020

J NeuroEngineering Rehabil

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Background: People use various strategies to maintain balance, such as taking a reactive step or rotating the upper body. To gain insight in human balance control, it is useful to know what makes people switch from one strategy to another. In previous studies the transition from a non-stepping balance response to reactive stepping was often described by an (extended) inverted pendulum model using a limited number of features. The goal of this study is to predict whether people will take a reactive step to recover from a push and to investigate what features are most relevant for that prediction by using a data-driven approach. Methods: Ten subjects participated in an experiment in which they received forward pushes to which they had to respond naturally with or without stepping. The collected kinematic and center of pressure data were used to train several classification algorithms to predict reactive stepping. The classification algorithms that performed best were used to determine the most important features through recursive feature elimination. Results: The neural networks performed better than the other classification algorithms. The prediction accuracy depended on the length of the observation time window: the longer the allowed time between the push and the prediction, the higher the accuracy. Using a neural network with one hidden layer and eight neurons, and a feature set consisting of various kinematic and center of pressure related features, an accuracy of 0.91 was obtained for predictions made up until the moment of step leg unloading, in combination with a sensitivity of 0.79 and a specificity 0.97. The most important features were the acceleration and velocity of the center of mass, and the position of the cervical joint center. Conclusion: Using our classification-based method the occurrence of reactive stepping could be predicted with a high accuracy, higher than previous methods for predicting natural reactive stepping. The feature set used for that prediction was different from the ones reported in other step prediction studies. Given the high step prediction performance, our method has the potential to be used for triggering reactive stepping in balance controllers of bipedal robots (e.g. exoskeletons).

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“…This made the model come to a stop, rather than have it walk. A comparison between this control scheme and human experimental data from literature was provided in (Aftab et al, 2016), for a forward fall from a standing position (tether-release). Absolute errors for the first recovery step were typically in the order of 10 cm for step length, and 0.02 s for step time throughout a range of initial body lean angles.…”

Section: Human-like Simultaneous Location and Timing Modulationmentioning

confidence: 99%

Foot placement in balance recovery

Vlutters¹

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Balance Recovery Prediction with Multiple Strategies for Standing Humans

Cited by 25 publications

References 36 publications

Human-like Balance Recovery Based on Numerical Model Predictive Control Strategy

Human-like Balance Recovery Based on Numerical Model Predictive Control Strategy

Predicting reactive stepping in response to perturbations by using a classification approach

Foot placement in balance recovery

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