An elaborate data set on human gait and the effect of mechanical perturbations

Moore, Jason K.; Hnat, Sandra K.; Bogert, Antonie J. van den

doi:10.7287/peerj.preprints.700v4

Cited by 4 publications

(3 citation statements)

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“…Since both the corrected leg power and summed joint power were calculated using the same GRFs, and the respective curves for the contralateral leg on the perturbed stride were in better agreement ( R 2 = 0.90 for early stance perturbations, 0.98 for late stance perturbations), the discrepancy between them is likely related to the centre of pressure deviations altering moments during the perturbation. These deviations could be caused by acceleration or deceleration of the treadmill rollers inducing a moment and altering the centre of pressure [48,49]. Additionally, modelling assumptions could propagate moment and power discrepancies from the foot to other joints.…”

Section: Discussionmentioning

confidence: 99%

Which lower limb joints compensate for destabilizing energy during walking in humans?

Golyski

Sawicki

2022

J. R. Soc. Interface.

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Current approaches to investigating stabilizing responses during locomotion lack measures that both directly relate to perturbation demands and are shared across different levels of description (i.e. joints and legs). Here, we investigated whether mechanical energy could serve as a ‘common currency’ during treadmill walking with transient unilateral belt accelerations. We hypothesized that by delivering perturbations in either early or late stance, we could elicit net negative or positive work, respectively, from the perturbed leg at the leg/treadmill interface, which would dictate the net demand at the overall leg level. We further hypothesized that of the lower limb joints, the ankle would best reflect changes in overall leg work. On average across all seven participants and 222 perturbations, we found early stance perturbations elicited no change in net work performed by the perturbed leg on the treadmill, but net positive work by the overall leg, which did not support our hypotheses. Conversely, late stance perturbations partially supported our hypotheses by eliciting positive work at the leg/treadmill interface, but no change in net work by the overall leg. In support of our final hypothesis, changes in perturbed ankle work, in addition to contralateral knee work, best reflected changes in overall leg work.

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Section: Discussionmentioning

confidence: 99%

Which lower limb joints compensate for destabilizing energy during walking in humans?

Golyski

Sawicki

2022

J. R. Soc. Interface.

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“…on the uniqueness of the dataset. Most gait datasets currently available fall under the following categories: (1) Full-body kinematics or kinetics from level-ground walking or running in a lab environment, comprised of straight walking (~10 m) at different self-selected speeds [7][8][9][10][11] , (2) full-body kinematics or kinetics from treadmill walking or running at pre-defined speeds 7,[11][12][13] , (3) standing data collected on perturbation platforms 14,15 , and (4) staircase walking on in-lab staircases with 4-7 steps 11,16 . For a more comprehensive review of stair ambulation, please refer to 17 .…”

Section: Background and Summarymentioning

confidence: 99%

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

et al. 2023

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In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.

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“…These gait patterns corresponded to an average maximum range of motion in the sagittal plane of 45.6 o , 61.5 o , and 34.4 o for the hip, knee, and ankle joints, respectively. This dataset is representative of a wide range of healthy gait patterns with similar joint angle range [79,80]. The subjects were recorded using a motion capture system over five trials of walking at a self-selected natural walking speed.…”

Section: Desired Gait Datasetmentioning

confidence: 99%

A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

2021

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Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

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An elaborate data set on human gait and the effect of mechanical perturbations

Cited by 4 publications

References 0 publications

Which lower limb joints compensate for destabilizing energy during walking in humans?

Which lower limb joints compensate for destabilizing energy during walking in humans?

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

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