![]() 21 recorded electroencephalography, lower-limb electromyography (EMG), and full body kinematics for ten participants walking on level-ground, ramps, and stairs. Non-steady conditions including transitions between locomotion modes and continuous variations of slopes and speeds are critical to modeling human locomotion and designing agile robotic prostheses.Īlthough the biomechanics of able-bodied walking 13, running 15, sit to stand 20, and stair climbing 16 have been well-documented as independent locomotion tasks, it is difficult to combine datasets due to differences in the measurements, methods, and participants. In fact, 75 of all walking bouts are less than 40 steps in a row 19. However, real-life human locomotion is far from steady state, involving intermittent bouts of walking, stopping, sitting, standing, and stair climbing 18. State-of-art control systems for robotic prosthetic legs are similarly limited to a small set of steady-state locomotion tasks, using finite state machines to control instantaneous transitions between them (risking classification errors and jerky motion at transition points 17). Most studies of able-bodied human locomotion report lower-limb kinematics and kinetics during a limited set of steady-state tasks, e.g., walking 13, 14, running 15, or stair ascent and descent 16, with only a few discrete samples of speed and/or incline for each task 14. Although wearable robotic devices can have different goals (e.g., reducing energetic cost 6, 7), able-bodied data are often used as reference trajectories in the control system 8, 9, 10, 11, 12 to restore normative biomechanics in impaired individuals, such as individuals with lower-limb amputation, whose biomechanics are likely to be significantly altered. To address limitations in amputee locomotion 1, robotic prosthetic legs are being developed with specifications for design and control based on able-bodied human biomechanics data 2, 3, 4, 5. ![]() Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill. This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (☐° 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 (☐.2 0.5), and stair ascent/descent with multiple stair inclines (20° 25° 30° and 35°). However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. 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. ![]() 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.
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