Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines

Abstract

Previous work has shown that it is possible to use a mechanical phase variable to accurately quantify the progression through a human gait cycle, even in the presence of disturbances. However, mechanical phase variables are highly dependent on the behavior of the body segment from which they are measured, which can change with the human's task or in response to different disturbances. In this study, we compare kinematic parameterization methods based on time, thigh phase angle, and tibia phase angle with motion capture data obtained from ten able-bodied subjects walking at three inclines while experiencing phase-shifting perturbations from a split-belt instrumented treadmill. The belt, direction, and timings of perturbations were quasi-randomly selected to prevent anticipatory action by the subjects and sample different types of perturbations. Statistical analysis revealed that both phase parameterization methods are superior to time parameterization, with thigh phase angle also being superior to tibia phase angle in most cases.

Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization

“…One of the methods for estimation of %GC without explicit reliance on time is to use a phase variable that encodes progression in the gait cycle ( Villarreal and Gregg, 2014 ). Different candidate variables have been proposed and investigated in the literature, including the forward progression of center of pressure (for stance phase only) ( Gregg and Sensinger, 2013 ; Gregg et al, 2014 ), horizontal position of the hip joint ( Ames, 2012 ; Quintero et al, 2015 ), and the polar angles of the phase portrait (i.e., 2D plot of a state variable versus its time derivative) of different joint or segment angles ( Villarreal and Gregg, 2014 ; Macaluso et al, 2021 ). For brevity, we will use the term “phase angle” to refer to the last one.…”

“…In addition to being time-independent, using phase variables can potentially offer better synchronization in non-steady gait and even during perturbations ( Villarreal and Gregg, 2016 ), as the instantaneous gait phase information is assumed to be directly encoded in the phase variable, thus eliminating the need for convergence over several gait cycles. These methods have mostly been explored in previous works focused on estimation only ( Quintero et al, 2017 ; Macaluso et al, 2021 ) or in prosthesis control ( Holgate et al, 2009 ; Quintero et al, 2018 ; Hong et al, 2021 ); studies about the performance of the exoskeleton control methods based on them are more scarce, as will be discussed in detail in Section 2.1.2 . For hip exoskeletons, the hip joint angle in the sagittal plane can be used as a basis for gait phase estimation.…”

A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

“…On our collected dataset, the proposed method was compared with two previous gait phase estimation methods, denoted by φ & φ and φ & φ, respectively. These two methods were chosen for comparison because they were typically and widely used in recent gait phase estimation works [6], [16]- [18], [21] and have been validated through robotic experiments on prostheses, thus can represent the state of the art. Some other references in this paper [23], [26] were not considered for comparison because the former one has not been tested on a prosthesis and the latter one was designed only for stance phase.…”

“…Hone et al [18] also reported that the thigh angle profile and its integral were phase-shifted sinusoids and an additional phase correction method was required for accurate gait phase estimation. The gait phase can also be estimated from the pair of thigh angle and its angular velocity [19]- [21], but the measured angular velocity is prone to be noisy and make estimation inaccurate. The thigh angle can also be directly utilized to estimate the gait phase and it is reported to be more stable than the integral and the angular velocity [5], [8], [22], but there was an obvious saturation of the estimated gait phase in the late swing phase in the previous works, which was caused by the retraction of the thigh before the heel-strike event and affected the estimation accuracy.…”

Motor Adaptation Effect of Active Joint Movement During Cycling Generated by Ankle Assist Ergometer: A Proof-of-Concept Study in Healthy Participants

“…Such coordination allows us to control the movements of multiple joints. By applying the underlying mechanisms of interlimb and intralimb coordination, previous studies using split-belt treadmills (separated double-belt treadmills) have clarified the effect of changes in joint movement during walking on non-disabled people [4], [5] and patients suffering from movement disorders [6]. Ergometer training through cyclic movements such as walking can promote walking-like muscle activity and is associated with walking function improvement [7], [8], [9].…”