Gait Parameter Control Timing with Dynamic Manual Contact or Visual Cues

We investigated the timing of gait parameter changes (stride length, peak toe velocity, and double-, single-support, and complete step duration) to control gait speed. Eleven healthy participants adjusted their gait speed on a treadmill to maintain a constant distance between them and a fore-aft oscillating cue (a place on a conveyor belt surface). The experimental design balanced conditions of cue modality (vision: eyes-open; manual contact: eyes-closed while touching the cue); treadmill speed (0.2, 0.4, 0.85, and 1.3 m/s); and cue motion (none, ±10 cm at 0.09, 0.11, and 0.18 Hz). Correlation analyses revealed a number of temporal relationships between gait parameters and cue speed. The results suggest that neural control ranged from feedforward to feedback. Specifically, step length preceded cue velocity during double-support duration suggesting anticipatory control. Peak toe velocity nearly coincided with its most-correlated cue velocity during single-support duration. The toe-off concluding step and double-support durations followed their most-correlated cue velocity, suggesting feedback control. Cue-tracking accuracy and cue velocity correlations with timing parameters were higher with the manual contact cue than visual cue. The cue/gait timing relationships generalized across cue modalities, albeit with greater delays of step-cycle events relative to manual contact cue velocity. We conclude that individual kinematic parameters of gait are controlled to achieve a desired velocity at different specific times during the gait cycle. The overall timing pattern of instantaneous cue velocities associated with different gait parameters is conserved across cues that afford different performance accuracies. This timing pattern may be temporally shifted to optimize control. Different cue/gait parameter latencies in our nonadaptation paradigm provide general-case evidence of the independent control of gait parameters previously demonstrated in gait adaptation paradigms.