Dataset Information

A kinematic and kinetic dataset of 18 above-knee amputees walking at various speeds.

ABSTRACT: Motion capture is necessary to quantify gait deviations in individuals with lower-limb amputations. However, access to the patient population and the necessary equipment is limited. Here we present the first open biomechanics dataset for 18 individuals with unilateral above-knee amputations walking at different speeds. Based on their ability to comfortably walk at 0.8?m/s, subjects were divided into two groups, namely K2 and K3. The K2 group walked at [0.4, 0.5, 0.6, 0.7, 0.8] m/s; the K3 group walked at [0.6, 0.8, 1.0, 1.2, 1.4] m/s. Full-body biomechanics was collected using a 10-camera motion capture system and a fully instrumented treadmill. The presented open dataset will enable (i) clinicians to understand the biomechanical demand required to walk with a knee and ankle prosthesis at various speeds, (ii) researchers in biomechanics to gain new insights into the gait deviations of individuals with above-knee amputations, and (iii) engineers to improve prosthesis design and function.

SUBMITTER: Hood S

PROVIDER: S-EPMC7242470 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A kinematic and kinetic dataset of 18 above-knee amputees walking at various speeds.

Hood Sarah S Ishmael Marshall K MK Gunnell Andrew A Foreman K B KB Lenzi Tommaso T

Scientific data 20200521 1

Motion capture is necessary to quantify gait deviations in individuals with lower-limb amputations. However, access to the patient population and the necessary equipment is limited. Here we present the first open biomechanics dataset for 18 individuals with unilateral above-knee amputations walking at different speeds. Based on their ability to comfortably walk at 0.8 m/s, subjects were divided into two groups, namely K2 and K3. The K2 group walked at [0.4, 0.5, 0.6, 0.7, 0.8] m/s; the K3 group ...[more]

PMID: 32439980

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Project description:Lower extremity powered exoskeletons (LEPE) are an emerging technology that assists people with lower-limb paralysis. LEPE for people with complete spinal cord injury walk at very slow speeds, below 0.5m/s. For the able-bodied population, very slow walking uses different neuromuscular, locomotor, postural, and dynamic balance control. Speed dependent kinetic and kinematic regression equations in the literature could be used for very slow walking LEPE trajectory scaling; however, kinematic and kinetic information at walking speeds below 0.5 m/s is lacking. Scaling LEPE trajectories using current reference equations may be inaccurate because these equations were produced from faster than real-world LEPE walking speeds. An improved understanding of how able-bodied people biomechanically adapt to very slow walking will provide LEPE developers with more accurate models to predict and scale LEPE gait trajectories. Full body motion capture data were collected from 30 healthy adults while walking on an instrumented self-paced treadmill, within a CAREN-Extended virtual reality environment. Kinematic and kinetic data were collected for 0.2 m/s-0.8 m/s, and self-selected walking speed. Thirty-three common sagittal kinematic and kinetic gait parameters were identified from motion capture data and inverse dynamics. Gait parameter relationships to walking speed, cadence, and stride length were determined with linear and quadratic (second and third order) regression. For parameters with a non-linear relationship with speed, cadence, or stride-length, linear regressions were used to determine if a consistent inflection occurred for faster and slower walking speeds. Group mean equations were applied to each participant's data to determine the best performing equations for calculating important peak sagittal kinematic and kinetic gait parameters. Quadratic models based on walking speed had the strongest correlations with sagittal kinematic and kinetic gait parameters, with kinetic parameters having the better results. The lack of a consistent inflection point indicated that the kinematic and kinetic gait strategies did not change at very slow gait speeds. This research showed stronger associations with speed and gait parameters then previous studies, and provided more accurate regression equations for gait parameters at very slow walking speeds that can be used for LEPE joint trajectory development.

Dataset Information

A kinematic and kinetic dataset of 18 above-knee amputees walking at various speeds.

Publications

A kinematic and kinetic dataset of 18 above-knee amputees walking at various speeds.

Similar Datasets

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