On the relation between joint moments and pedalling rates at constant power in bicycling

Redfield, Rob; Hull, M.L.

doi:10.1016/0021-9290(86)90008-4

Cited by 113 publications

(52 citation statements)

References 10 publications

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“…The third and fourth terms restricted θ and θ̇ to remain within the physical limits determined by experimental data (Luttgens and Hamilton, 1997). To facilitate convergence in this first attempt to simulate gait and slip recovery, we adopted a common scheme (Nubar and Contini, 1961;Redfield and Hull, 1986) to minimize the integral of the square of the torque (term 5). The sixth and seventh terms constrained the horizontal velocity and acceleration of the right heel to remain below the corresponding peak values observed in gait experiments.…”

Section: At Eachmentioning

confidence: 99%

Predicted threshold against backward balance loss following a slip in gait

Yang

Anderson

Pai

2008

Journal of Biomechanics

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The purpose of this study was to use a 7-link, moment-actuated human model to predict, at liftoff of the trailing foot in gait, the threshold of the center of mass (COM) velocity relative to the base of support (BOS) required to prevent backward balance loss during single stance recovery from a slip. Five dynamic optimization problems were solved to find the minimum COM velocities that would allow the simulation to terminate with the COM above the BOS when the COM started 0.25, 0.5, 0.75, 1.0, and 1.25 foot lengths behind the heel of the stance foot (i.e., behind the BOS). The initial joint angles of the model were based on averaged data from experimental trials. Foot-ground contact was modeled using 16 visco-elastic springs distributed under the stance foot. Slipping was modeled by setting the sliding coefficient of friction of these springs to 0.02. The forward velocity of the COM necessary to avoid a backward balance loss is nearly two times larger under slip conditions than under non-slip conditions. The predicted threshold for backward balance loss following a slip agreed well with experimental data collected from 99 young adults in response to 927 slips during walking. In all trials in which a subject's COM had a velocity below the predicted threshold, the subject's recovery foot landed posterior to the slipping foot as predicted. Finally, combining experimental data with optimization, we verified that the 7-link model could more accurately predict gait stability than a 2-link model.

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Section: At Eachmentioning

confidence: 99%

Predicted threshold against backward balance loss following a slip in gait

Yang

Anderson

Pai

2008

Journal of Biomechanics

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“…A conversion from the global to the crank coordinate system was later conducted to compute the effective (crank tangential) and radial forces in order to compute the index of effectiveness 22 . Conventional inverse dynamics were conducted to calculate the net joint moments at the ankle and at the knee joints 23 using adapted scripts of van den Bogert and De Koning 24 . Quadriceps muscle force was estimated by the ratio between the knee extensor moment and the moment arm of the patellar tendon 25 , only when the net knee moment was an extensor moment.…”

Section: Discussionmentioning

confidence: 99%

Differences in Pedaling Technique in Cycling: A Cluster Analysis

Lanferdini¹,

Bini²,

Figueiredo³

et al. 2016

International Journal of Sports Physiology and Performance

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The purpose of this study was to employ cluster analysis to assess if cyclists would opt for different strategies in terms of neuromuscular patterns when pedalling at the power output of their second ventilatory threshold (POVT2) compared to cycling at their maximal power output (POMAX). Twenty athletes performed an incremental cycling test to determine their power output (POMAX and POVT2; first session) and pedal forces, muscle activation, muscle-tendon unit length, and vastus lateralis architecture (fascicle length, pennation angle, and muscle thickness) were recorded (second session) in POMAX and POVT2. Athletes were assigned to two clusters based on the behaviour of outcome variables at POVT2 and POMAX using cluster analysis. Clusters #1 (n=14) and #2 (n=6) showed similar power output and oxygen uptake. Cluster #1 presented larger increases in pedal force and knee power than Cluster #2, without differences for the index of effectiveness. Cluster #1 presented less variation in knee angle, muscle-tendon unit length, pennation angle and tendon length compared to Cluster #2. However, Clusters #1 and #2 showed similar muscle thickness, fascicle length and muscle activation. When cycling at POVT2 versus POMAX, cyclists could opt for keeping a constant knee power and pedal force production, associated with an increase in tendon excursion and a constant fascicle length. In conclusion, increases in power output lead to greater variations in knee angle, muscle-tendon unit length, tendon length, and pennation angle of vastus lateralis for a similar knee extensor activation and smaller pedal force changes in cyclists from Cluster #2 compared to Cluster #1.

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“…The application of pedaling forces has been previously studied in normal (12,13) and prolonged cycling conditions (14). The typical pedaling stroke is separated into the effective (force application perpendicular to crank arm) and ineffective (force application parallel with crank arm) components (1).…”

Section: Discussionmentioning

confidence: 99%