Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

Abstract

This research aimed to determine whether: (1) shoe-worn magnetic and inertial sensors can be used to detect hurdle clearance and identify the leading leg in 400-m hurdles, and (2) to provide an analysis of the hurdlers’ spatiotemporal parameters in the intervals defined by the hurdles’ position. The data set is composed of MIMU recordings of 15 athletes in a competitive environment. The results show that the method based on the duration of the flight phase was able to detect hurdle clearance and identify the leading leg with 100% accuracy. Moreover, by combining the swing phase duration with the orientation of the foot, we achieved, in unipedal configuration, 100% accuracy in hurdle clearance detection, and 99.7% accuracy in the identification of the leading leg. Finally, this study provides statistical evidence that contact time significantly increases, while speed and step frequency significantly decrease with time during 400 m hurdle races.

Reply to Comments: Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor

“…The current document answers the comment addressed by Schmidt, M., Nolte, K., Alt, T., and Jaitner, T. regarding our recent paper "Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor" (Sensors 2020, 20, 354) [1].…”

“…The range of speeds observed in [1] was greater than for the validation study [2]; therefore, we could not obtain the actual bias at the observed running speeds.…”

“…As we discussed in [2] and was appropriately reported in the comments, the Inertial Measurement Unit (IMU) based system underestimates CT, with a bias that depends on the running speed. Although we were aware of these results in [1], we did not correct the bias for CT estimations for the following reasons:…”

“…The results from [2] indicate that the bias decreases as the running speed increases. Hence, we extrapolated the linear trend of the bias and assumed that it would be relatively low at the running speeds observed in [1].…”

“…The correction based on the running speed would require a measure of the instantaneous speed to apply the appropriate correction at each step. The current study [1] estimates the average running speed between the hurdles. We expected the speed to vary between the hurdles and therefore did not use the average speed to reduce the bias of CT and FLY.…”