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The zero-velocity update (ZUPT) method has become a popular approach to estimate foot kinematics from foot worn inertial measurement units (IMUs) during walking and running. However, the accuracy of the ZUPT method for stride parameters at sprinting speeds remains unknown, specifically when using sensors with characteristics well suited for sprinting (i.e., high accelerometer and gyroscope ranges and sampling rates). Seventeen participants performed 80-meter track sprints while wearing a Blue Trident IMeasureU IMU. Two cameras, at 20 and 70 meters from the start, were used to validate the ZUPT method on a stride-by-stride and on a cumulative distance basis. In particular, the validity of the ZUPT method was assessed for: (1) estimating a single stride length attained near the end of an 80m sprint (i.e., stride at 70m); (2) estimating cumulative distance from ∼20 to ∼70 m; and (3) estimating total distance traveled for an 80-meter track sprint. Individual stride length errors at the 70-meter mark were within -6% to 3%, with a bias of -0.27%. Cumulative distance errors were within -4 to 2%, with biases ranging from -0.85 to -1.22%. The results of this study demonstrate the ZUPT method provides accurate estimates of stride length and cumulative distance traveled for sprinting speeds. [ABSTRACT FROM AUTHOR]

Older adults and neurological populations tend to walk with slower speeds, more gait variability, and a higher metabolic cost. This higher metabolic cost could be related to their increased gait variability, but this relationship is still unclear. The purpose of this study was to determine how increased step length variability affects the metabolic cost of waking. Eighteen healthy young adults completed a set of 5-minute trials of treadmill walking at 1.20 m/s while we manipulated their step length variability. Illuminated rectangles were projected onto the surface of a treadmill to cue step length variabilities of 0, 5 and 10% (coefficient of variation). Actual step lengths and their variability were tracked with reflective markers on the feet, while metabolic cost was measured using indirect calorimetry. Changes in metabolic cost across habitual walking (no projections) and the three variability conditions were analyzed using a linear mixed effects model. Metabolic power was largest in the 10% condition (4.30 ± 0.23 W/kg) compared to 0% (4.16 ± 0.18 W/kg) and habitual (3.98 ± 0.25 W/kg). The participant's actual step length variability did not match projected conditions for 0% (3.10%) and 10% (7.03%). For every 1% increase in step length variability, there is an 0.7% increase in metabolic cost. Our results demonstrate an association between the metabolic cost of walking and gait step length variability. This suggests that increased gait variability contributes to a portion of the increased cost of walking seen in older adults and neurological populations., Competing Interests: Competing interests The authors declare no competing or financial interests.