There is an ongoing debate about the reasons underlying gait transition in terrestrial locomotion. In bipedal locomotion, the 'compass gait', a reductionist model of inverted pendulum walking, predicts the boundaries of speed and step length within which walking is feasible. The stance of the compass gait is energetically optimal-at walking speeds-owing to the absence of leg compression/extension; completely stiff limbs perform no work during the vaulting phase. Here, we extend theoretical compass gait vaulting to include inclines, and find good agreement with previous observations of changes in walk-run transition speed (approx. 1% per 1% incline). We measured step length and frequency for humans walking either on the level or up a 9.8 per cent incline and report preferred walk-run, walk-compliant-walk and maximum walk-run transition speeds. While the measured 'preferred' walk-run transition speed lies consistently below the predicted maximum walking speeds, and 'actual' maximum walking speeds are clearly above the predicted values, the onset of compliant walking in level as well as incline walking occurs close to the predicted values. These findings support the view that normal human walking is constrained by the physics of vaulting, but preferred absolute walk-run transition speeds may be influenced by additional factors.
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http://dx.doi.org/10.1098/rsbl.2012.1121 | DOI Listing |
PeerJ
October 2024
Biomechanics and Movement Analysis Research Laboratory, Department of Biological Sciences, CENUR Litoral Norte, Universidad de la República, Paysandú, Paysandú, Uruguay.
Background: Muscular synergies could represent the patterns of muscular activation used by the central nervous system (CNS) to simplify the production of movement. Studies in walking-running transitions described up to nine synergy modules, and an earlier activation of flexor and extension ankle muscular groups compared to running or walking. Our project aims to study the behaviour of muscle synergies in different stance and swing variations of walking-running (WRT) and running-walking (RWT) transitions.
View Article and Find Full Text PDFPLoS One
June 2024
University Hospitals, Child Neurology, Leuven, Belgium.
PLoS One
April 2023
Centre for Elite Sports Research, Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
In cross-country skiing, athletes use different techniques akin to locomotor gaits such as walking and running. Transitions between these techniques generally depend on speed and incline, in a similar way as walk-run transitions. Previous studies have examined the roles of incline, speed, and mechanical power demand in triggering transitions.
View Article and Find Full Text PDFJ Neuromuscul Dis
May 2023
Department of Pediatrics, Northwestern University Feinberg School of Medicine, USA.
Background: Ambulatory individuals with spinal muscular atrophy experience weakness and impairments of speed and endurance. This leads to decreased motor skill performance required for daily living including transitioning from floor to stand, climbing stairs, and traversing short and community distances. Motor function improvements have been reported in individuals receiving nusinersen, but changes in timed functional tests (TFTs) which assess shorter distance walking and transitions have not been well documented.
View Article and Find Full Text PDFFront Neurorobot
January 2023
Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Generating multimodal locomotion in underactuated bipedal robots requires control solutions that can facilitate motion patterns for drastically different dynamical modes, which is an extremely challenging problem in locomotion-learning tasks. Also, in such multimodal locomotion, utilizing body morphology is important because it leads to energy-efficient locomotion. This study provides a framework that reproduces multimodal bipedal locomotion using passive dynamics through deep reinforcement learning (DRL).
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