Dataset Information

Lower-limb kinematics and kinetics during continuously varying human locomotion.

ABSTRACT: Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.

SUBMITTER: Reznick E

PROVIDER: S-EPMC8553836 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Lower-limb kinematics and kinetics during continuously varying human locomotion.

Reznick Emma E Embry Kyle R KR Neuman Ross R Bolívar-Nieto Edgar E Fey Nicholas P NP Gregg Robert D RD

Scientific data 20211028 1

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/ ...[more]

PMID: 34711856

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Project description:Static lower extremity alignment (LEA) during normal stance has been used clinically as a tool to determine the presence of known anterior cruciate ligament (ACL) risk factors during dynamic tasks. Previous work investigating the relationship between static LEA during normal stance and risk factors for ACL injury is limited by the use of imprecise methods or because it focuses on knee valgus only and no other potentially important variables. The aim of this investigation was to determine the relationships between static LEA and the corresponding LEA during drop landings. Forty-one female athletes were recruited for the study (age: 19.8 ± 2.5 years, height: 1.73 ± 0.06 m, mass: 64.03 ± 6.66 kg). Lower limb kinematic data were collected using a 10 camera infrared motion capture system (500 Hz) with retro-reflective markers placed over key anatomical landmarks. This system was linked to two force platforms (1000 Hz) with subsequent three-dimensional kinematic and kinetic data developed using standard software (Visual3D). Following an appropriate warm-up, data collection involved participants standing with their arms partially abducted to record static LEA. This was following by a series of drop landings from a 0.4 m box onto the force platforms. Maximum LEA data during drop landings were then compared with static LEA. Analyses showed that in comparison to static stance, during landings the anterior tilt of the pelvis decreased while hip abduction and knee internal rotation increased. At best, static LEA variables were moderately correlated (r = -0.51 to 0.58) with peak values measured during drop landings. Additionally, regression analysis did not yield any significant predictors of any key peak hip or knee variables measured during drop landings (p = 0.15 to 0.89). When combined, the poor relationships observed between kinematics during static LEA and LEA during drop landings calls into question the practice of using static measures to predict LEA during even simple landing tasks. These findings suggest static assessments of LEA may have minimal value as an ACL injury screening tool.

Project description:Purpose:Modern statistics and higher computational power have opened novel possibilities to complex data analysis. While gait has been the utmost described motion in quantitative human motion analysis, descriptions of more challenging movements like the squat or lunge are currently lacking in the literature. The hip and knee joints are exposed to high forces and cause high morbidity and costs. Pre-surgical kinetic data acquisition on a patient-specific anatomy is also scarce in the literature. Studying the normal inter-patient kinetic variability may lead to other comparable studies to initiate more personalized therapies within the orthopedics. Methods:Trials are performed by 50 healthy young males who were not overweight and approximately of the same age and activity level. Spatial marker trajectories and ground reaction force registrations are imported into the Anybody Modeling System based on subject-specific geometry and the state-of-the-art TLEM 2.0 dataset. Hip and knee joint reaction forces were obtained by a simulation with an inverse dynamics approach. With these forces, a statistical model that accounts for inter-subject variability was created. For this, we applied a principal component analysis in order to enable variance decomposition. This way, noise can be rejected and we still contemplate all waveform data, instead of using deduced spatiotemporal parameters like peak flexion or stride length as done in many gait analyses. In addition, this current paper is, to the authors' knowledge, the first to investigate the generalization of a kinetic model data toward the population. Results:Average knee reaction forces range up to 7.16 times body weight for the forwarded leg during lunge. Conversely, during squat, the load is evenly distributed. For both motions, a reliable and compact statistical model was created. In the lunge model, the first 12 modes accounts for 95.26% of inter-individual population variance. For the maximal-depth squat, this was 95.69% for the first 14 modes. Model accuracies will increase when including more principal components. Conclusion:Our model design was proved to be compact, accurate, and reliable. For models aimed at populations covering descriptive studies, the sample size must be at least 50.

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Lower-limb kinematics and kinetics during continuously varying human locomotion.

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Lower-limb kinematics and kinetics during continuously varying human locomotion.

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