Predicting multiple step placements for human balance recovery tasks

Methods for integrating postural control into biomechanical human simulations: a systematic review

Understanding of the human body's internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems' information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models' level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.

Identification of a Step-And-Brake Controller of a Human Based on Prediction of Capturability

An explicit mathematical form of a human’s step-and-brake controller is identified through motion measurement of the human subject. The controller was originally designed for biped robots based on the reduced-order dynamics and the model predictive control scheme with the terminal capturability condition, and is compatible with both stand-still and stepping motions. The minimal number of parameters facilitates the identification from measured trajectories of the center of mass and the zero-moment point of the human subject. In spite of the minimality, the result only suited the human’s behaviors well with slight modifications of the model by taking direction-dependency of the natural falling speed and the inertial torque about the center of mass into account. Furthermore, the parameters are successfully identified even from the first half of motion sequence, which means that the proposed method is available in designing on-the-fly systems to evaluate balancing abilities of humans and to assist balances of humans in walking.

Predicting foot placement for balance through a simple model with swing leg dynamics

Stepping is one important strategy to restore balance against external perturbations. Although current literature have proposed models to predict the recovery foot placement, swing leg actuation is rarely taken into account. In this paper, we combine the capturability-based analysis with swing leg dynamics and seek to contribute to the following problem: for a biped system recovering balance from external perturbations, how to choose a step position and duration in minimizing swing actuation cost? We expand the linear inverted pendulum model with an actuated linear pendulum mounted on the pelvis, the addition of which is proposed to describe the swing leg dynamics. The closed-form expression of swing actuation with constraints is derived from the explicit formulations of the pelvis and swing foot motion. We calculate the optimal step position and duration to minimize swing cost under various perturbations. Results show that the optimal step duration keeps constant, while the optimal step position is linearly proportional to the magnitude of perturbations. Such findings match well with experimental data from ten subjects delivered with waist-perturbations. These current results demonstrate that our proposed model with swing dynamics suggests an effective alternative to predict recovery foot placement of biped systems following unexpected perturbations.

Balance Recovery Prediction with Multiple Strategies for Standing Humans

Human balance recovery from external disturbances is a complex process, and simulating it remains an open challenge. In particular, there still is a need for a comprehensive numerical tool capable of predicting the outcome of a balance perturbation, including in particular the three elementary recovery strategies: ankle, hip and stepping with variable step duration. In order to fill this gap we further developed a previously proposed multiple step balance recovery prediction tool to include the use of the hip strategy and variable step duration. Simulated recovery reactions are compared against observations from different experimental situations from the literature. Reasonable accuracy in terms of step positions and durations were obtained for these different situations using a single set of controller parameters. Moreover, variations in the use of the hip strategy and the step duration between situations were consistent with biomechanical observations. Such a model could be useful to better understand the balance recovery mechanisms, and could also be used to identify potentially hazardous situations.

Biomechanics of Step Initiation After Balance Recovery With Implications for Humanoid Robot Locomotion

Balance-recovery stepping is often necessary for both a human and humanoid robot to avoid a fall by taking a single step or multiple steps after an external perturbation. The determination of where to step to come to a complete stop has been studied, but little is known about the strategy for initiation of forward motion from the static position following such a step. The goal of this study was to examine the human strategy for stepping by moving the back foot forward from a static, double-support position, comparing parameters from normal step length (SL) to those from increasing SLs to the point of step failure, to provide inspiration for a humanoid control strategy. Healthy young adults instrumented with joint reflective markers executed a prescribed-length step from rest while marker positions and ground reaction forces (GRFs) were measured. The participants were scaled to the Gait2354 model in opensim software to calculate body kinematic and joint kinetic parameters, with further post-processing in matlab. With increasing SL, participants reduced both static and push-off back-foot GRF. Body center of mass (CoM) lowered and moved forward, with additional lowering at the longer steps, and followed a path centered within the initial base of support (BoS). Step execution was successful if participants gained enough forward momentum at toe-off to move the instantaneous capture point (ICP) to within the BoS defined by the final position of both feet on the front force plate. All lower extremity joint torques increased with SL except ankle joint. Front knee work increased dramatically with SL, accompanied by decrease in back-ankle work. As SL increased, the human strategy changed, with participants shifting their CoM forward and downward before toe-off, thus gaining forward momentum, while using less propulsive work from the back ankle and engaging the front knee to straighten the body. The results have significance for human motion, suggesting the upper limit of the SL that can be completed with back-ankle push-off before additional knee flexion and torque is needed. For biped control, the results support stability based on capture-point dynamics and suggest strategy for center-of-mass trajectory and distribution of ground force reactions that can be compared with robot controllers for initiation of gait after recovery steps.

Controlling legs for locomotion-insights from robotics and neurobiology

Walking is the most common terrestrial form of locomotion in animals. Its great versatility and flexibility has led to many attempts at building walking machines with similar capabilities. The control of walking is an active research area both in neurobiology and robotics, with a large and growing body of work. This paper gives an overview of the current knowledge on the control of legged locomotion in animals and machines and attempts to give walking control researchers from biology and robotics an overview of the current knowledge in both fields. We try to summarize the knowledge on the neurobiological basis of walking control in animals, emphasizing common principles seen in different species. In a section on walking robots, we review common approaches to walking controller design with a slight emphasis on biped walking control. We show where parallels between robotic and neurobiological walking controllers exist and how robotics and biology may benefit from each other. Finally, we discuss where research in the two fields diverges and suggest ways to bridge these gaps.

By counteracting gravity, triceps surae sets both kinematics and kinetics of gait

In the single-stance phase of gait, gravity acting on the center of mass (CoM) causes a disequilibrium torque, which generates propulsive force. Triceps surae activity resists gravity by restraining forward tibial rotation thereby tuning CoM momentum. We hypothesized that time and amplitude modulation of triceps surae activity determines the kinematics (step length and cadence) and kinetics of gait. Nineteen young subjects participated in two experiments. In the gait initiation (GI) protocol, subjects deliberately initiated walking at different velocities for the same step length. In the balance-recovery (BR) protocol, subjects executed steps of different length after being unexpectedly released from an inclined posture. Ground reaction force was recorded by a large force platform and electromyography of soleus, gastrocnemius medialis and lateralis, and tibialis anterior muscles was collected by wireless surface electrodes. In both protocols, the duration of triceps activity was highly correlated with single-stance duration (GI, R (2) = 0.68; BR, R (2) = 0.91). In turn, step length was highly correlated with single-stance duration (BR, R (2) = 0.70). Control of CoM momentum was obtained by decelerating the CoM fall via modulation of amplitude of triceps activity. By modulation of triceps activity, the central nervous system (CNS) varied the position of CoM with respect to the center of pressure (CoP). The CoM-CoP gap in the sagittal plane was determinant for setting the disequilibrium torque and thus walking velocity. Thus, by controlling the gap, CNS-modified walking velocity (GI, R (2) = 0.86; BR, R (2) = 0.92). This study is the first to highlight that by merely counteracting gravity, triceps activity sets the kinematics and kinetics of gait. It also provides evidence that the surge in triceps activity during fast walking is due to the increased requirement of braking the fall of CoM in late stance in order to perform a smoother step-to-step transition.

By counteracting gravity, triceps surae sets both kinematics and kinetics of gait

In the single-stance phase of gait, gravity acting on the center of mass (CoM) causes a disequilibrium torque, which generates propulsive force. Triceps surae activity resists gravity by restraining forward tibial rotation thereby tuning CoM momentum. We hypothesized that time and amplitude modulation of triceps surae activity determines the kinematics (step length and cadence) and kinetics of gait. Nineteen young subjects participated in two experiments. In the gait initiation (GI) protocol, subjects deliberately initiated walking at different velocities for the same step length. In the balance-recovery (BR) protocol, subjects executed steps of different length after being unexpectedly released from an inclined posture. Ground reaction force was recorded by a large force platform and electromyography of soleus, gastrocnemius medialis and lateralis, and tibialis anterior muscles was collected by wireless surface electrodes. In both protocols, the duration of triceps activity was highly correlated with single-stance duration (GI, R (2) = 0.68; BR, R (2) = 0.91). In turn, step length was highly correlated with single-stance duration (BR, R (2) = 0.70). Control of CoM momentum was obtained by decelerating the CoM fall via modulation of amplitude of triceps activity. By modulation of triceps activity, the central nervous system (CNS) varied the position of CoM with respect to the center of pressure (CoP). The CoM-CoP gap in the sagittal plane was determinant for setting the disequilibrium torque and thus walking velocity. Thus, by controlling the gap, CNS-modified walking velocity (GI, R (2) = 0.86; BR, R (2) = 0.92). This study is the first to highlight that by merely counteracting gravity, triceps activity sets the kinematics and kinetics of gait. It also provides evidence that the surge in triceps activity during fast walking is due to the increased requirement of braking the fall of CoM in late stance in order to perform a smoother step-to-step transition.

Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics

The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.