Using a System Identification Approach to Investigate Subtask Control during Human Locomotion

Abstract

Here we apply a control theoretic view of movement to the behavior of human locomotion with the goal of using perturbations to learn about subtask control. Controlling one's speed and maintaining upright posture are two critical subtasks, or underlying functions, of human locomotion. How the nervous system simultaneously controls these two subtasks was investigated in this study. Continuous visual and mechanical perturbations were applied concurrently to subjects (n = 20) as probes to investigate these two subtasks during treadmill walking. Novel application of harmonic transfer function (HTF) analysis to human motor behavior was used, and these HTFs were converted to the time-domain based representation of phase-dependent impulse response functions (ϕIRFs). These ϕIRFs were used to identify the mapping from perturbation inputs to kinematic and electromyographic (EMG) outputs throughout the phases of the gait cycle. Mechanical perturbations caused an initial, passive change in trunk orientation and, at some phases of stimulus presentation, a corrective trunk EMG and orientation response. Visual perturbations elicited a trunk EMG response prior to a trunk orientation response, which was subsequently followed by an anterior-posterior displacement response. This finding supports the notion that there is a temporal hierarchy of functional subtasks during locomotion in which the control of upper-body posture precedes other subtasks. Moreover, the novel analysis we apply has the potential to probe a broad range of rhythmic behaviors to better understand their neural control.

From template to anchors: transfer of virtual pendulum posture control balance template to adaptive neuromuscular gait model increases walking stability

“…Such a higher level of control can also be found in the neural system like corticospinal layer. This aligns with findings in human walking experiments [38–41].…”

“…As shown in figure 8, the evolution of the models results in considerable enlargement of the basin of attraction, meaning increase in robustness against postural perturbations. A similar control hierarchy was observed in perturbation recovery when a small step inferred via visual scene is reflected in a decreasing erector spinae stimulation and a forward trunk rotation [38]. Furthermore, it was shown that the motor cortex (MCx) issues voluntary motor commands and mediates reflex-like responses to stretch upper limb muscles [39].…”

Frequency-Domain Subspace Identification of Linear Time-Periodic (LTP) Systems

“…In this paper, we introduce a frequency-domain subspacebased state-space identification method for linear time-periodic (LTP) systems. Many problems in engineering and biology, such as wind turbines [1], rotor bearing systems [2], aircraft models [3], locomotion [4,5], and power distribution networks [6] require the consideration of time-periodic dynamics. As such, the analysis, identification, and control of LTP systems have received considerable attention [7][8][9].…”

Harmonic transfer functions based controllers for linear time-periodic systems

“…The increased interest in periodic motion has also increased the necessity to develop novel tools for the analysis, identification and control of periodic systems (Farkas, 2013; Sandberg et al, 2005). Some examples of such periodic systems are wind turbines (Allen et al, 2011; Bottasso and Cacciola, 2015), helicopter rotors (Hwang, 1997; Siddiqi, 2001), power systems (Kwon et al, 2017; Mollerstedt and Bernhardsson, 2000a) and some nonlinear systems that exhibit periodic behavior around a stable limit cycle (Logan et al, 2016; Sracic and Allen, 2011; Uyanik, 2017). Note that standard linear time-invariant (LTI) analysis tools cannot capture the periodic nature of the system dynamics for these examples.…”