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.

The role of user preference in the customized control of robotic exoskeletons

“…Across naive participants, the mean standard deviation of preferred timing (1.5% gait cycle) corresponds to just 17 ms for the average stride time observed in this study. This variability is comparable with the mean stride-to-stride variability for the timing of biological peak ankle torque during treadmill walking, which is 0.9 to 1.5% gait cycle for speeds between 1 and 1.4 m/s ( 35 , 36 ). The mean standard deviation of preferred magnitude (1.7 Nm) represents 1.7% of the average peak biological ankle torque observed during walking ( 35 ).…”

“…Exoskeleton torque (Nm) biological ankle torque observed during walking (35). This variability corresponds to only 20 to 40% of the mean stride-to-stride variability for the magnitude of biological peak torque during treadmill walking, which is 3.8 to 7.3 Nm for speeds between 1 and 1.4 m/s (35,36). It is important to note that these results may represent the lower bound of an individual's precision.…”

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

“…Gait analysis is typically measured in a dedicated laboratory requiring specialized equipment and expertise. Laboratory three-dimensional (3D) motion capture systems rely on researcher expertise to perform data collection where required anthropometric measures such as stature, leg length, knee, and ankle breadth are required (Sun et al, 2020 ; Moreira et al, 2021 ; Reznick et al, 2021 ). Marker placement on specific anatomical landmarks is a critical skill that influences the accuracy of calculations derived from the collected data.…”

Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain

“…Because the dataset used to regress our gait model only contained steady walking data, other tasks such as running or start/stopping are not explicitly modeled. While in theory the presented EKF can account for sudden stops by estimating ṗ as zero, we believe the estimator will benefit from training with data including such gait transitions (e.g., [38]). As datasets grow to include more behaviors, our intention is to extend our continuous gait model with new task variables representing these other dimensions of human locomotion.…”