Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running

Ruiter, C.J. de; Oeveren, Ben T van; Francke, Agnieta; Zijlstra, P.J.; Dieën, Jaap H. van

doi:10.1371/journal.pone.0163023

Cited by 14 publications

(21 citation statements)

References 16 publications

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“…The time component t stance is in itself relevant since a runner can only produce forces to propel the BCoM during the stance phase. For running speeds between 8 and 20 km/ h, t stance has a strong negative curvilinear relation with speed with values typically ranging between 0.34 and 0.18 seconds (Carrard et al, 2018;Chapman et al, 2012;Concejero et al, 2013;Dorn et al, 2012;Forrester & Townend, 2015;García-Pinillos et al, 2018;Hoyt et al, 2000;Nummela et al, 2007;Pavei et al, 2017;Roche-Seruendo et al, 2018;Da Rosa et al, 2019;De Ruiter et al, 2016;Weyand et al, 2000) (Figure 3). Due to the reduction in t stance with increasing speed, the GRF curves are compressed along the time axis, with increased force amplitudes to attain a sufficient impulse to maintain speed (I = ∫(F∆t) Dorn et al, 2012;Hamner & Delp, 2013;Nummela et al, 2007;Weyand et al, 2000).…”

Section: Stance Timementioning

confidence: 98%

The biomechanics of running and running styles: a synthesis

Oeveren

Ruiter

Beek

et al. 2021

Sports Biomechanics

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Running movements are parametrised using a wide variety of devices. Misleading interpretations can be avoided if the interdependencies and redundancies between biomechanical parameters are taken into account. In this synthetic review, commonly measured running parameters are discussed in relation to each other, culminating in a concise, yet comprehensive description of the full spectrum of running styles. Since the goal of running movements is to transport the body centre of mass (BCoM), and the BCoM trajectory can be derived from spatiotemporal parameters, we anticipate that different running styles are reflected in those spatiotemporal parameters. To this end, this review focuses on spatiotemporal parameters and their relationships with speed, ground reaction force and whole-body kinematics. Based on this evaluation, we submit that the full spectrum of running styles can be described by only two parameters, namely the step frequency and the duty factor (the ratio of stance time and stride time) as assessed at a given speed. These key parameters led to the conceptualisation of a so-called Dual-axis framework. This framework allows categorisation of distinctive running styles (coined 'Stick', 'Bounce', 'Push', 'Hop', and 'Sit') and provides a practical overview to guide future measurement and interpretation of running biomechanics.

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Section: Stance Timementioning

confidence: 98%

The biomechanics of running and running styles: a synthesis

Oeveren

Ruiter

Beek

et al. 2021

Sports Biomechanics

Self Cite

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“…Subject-specific neural networks were also devised to assess the running speed in free-living conditions using only triaxial accelerometric measurements, but the model needed a calibration/learning phase for each runner and was validated for the mean speed using few trials (Herren et al, 1999 ). One study, however, exploited the personalized calibration and proposed a model based solely on the contact time (De Ruiter et al, 2016 ). Although the authors obtained a low root-mean-square error (<3%), these results were not instantaneous estimations but rather the average speed over bouts of 125 meters.…”

Section: Introductionmentioning

confidence: 99%

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Falbriard

Soltani

Aminian

2021

Front. Sports Act. Living

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The overground speed is a key component of running analysis. Today, most speed estimation wearable systems are based on GNSS technology. However, these devices can suffer from sparse communication with the satellites and have a high-power consumption. In this study, we propose three different approaches to estimate the overground speed in running based on foot-worn inertial sensors and compare the results against a reference GNSS system. First, a method is proposed by direct strapdown integration of the foot acceleration. Second, a feature-based linear model and finally a personalized online-model based on the recursive least squares' method were devised. We also evaluated the performance differences between two sets of features; one automatically selected set (i.e., optimized) and a set of features based on the existing literature. The data set of this study was recorded in a real-world setting, with 33 healthy individuals running at low, preferred, and high speed. The direct estimation of the running speed achieved an inter-subject mean ± STD accuracy of 0.08 ± 0.1 m/s and a precision of 0.16 ± 0.04 m/s. In comparison, the best feature-based linear model achieved 0.00 ± 0.11 m/s accuracy and 0.11 ± 0.05 m/s precision, while the personalized model obtained a 0.00 ± 0.01 m/s accuracy and 0.09 ± 0.06 m/s precision. The results of this study suggest that (1) the direct estimation of the velocity of the foot are biased, and the error is affected by the overground velocity and the slope; (2) the main limitation of a general linear model is the relatively high inter-subject variance of the bias, which reflects the intrinsic differences in gait patterns among individuals; (3) this inter-subject variance can be nulled using a personalized model.

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“…Additionally, footpod devices have been utilized in athletic populations for monitoring running technique [20], in the ambulatory monitoring of gait spatial parameters at a variety of inclines [21], and as an instrument in quantifying training load in trained runners [22]. While investigations have utilized these devices to generate data as noted above, relatively few studies provide evidence of the validity [23][24][25] and even fewer have determined reliability [24] of the footpod instruments.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Trail Walking and Running Tasks Using the Stryd Power Meter

et al. 2019

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Footpod monitors are wearable devices attaching to the shoe with the ability to sense oscillations in leg movement; however, few studies provide reliability. The purpose was to provide reliability data for outdoor tasks as measured by the Stryd Power Meter, which is a footpod monitor. Young healthy individuals (N=20, male n=12, female n=8) completed two 5-min self-paced walks along a trail, and two 5-min trail runs. Reliability of the tasks was determined using Coefficient of Variation (CV), Intraclass Correlation (ICC), and 95% confidence intervals (CI). Measures during trail running that returned a CV less than 10%, met the ICC threshold of 0.70, and displayed good to excellent 95% CI included pace, average elapsed power, average elapsed form power, average elapsed leg spring, and vertical oscillation. The only variable during walking to meet these criteria was maximal power (CV=4.02%, ICC=0.968, CI=0.902, 0.989). Running tasks completed on a trail generally return more consistent measures for variables that can be obtained from the Stryd footpod device than walking tasks.

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Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running

Cited by 14 publications

References 16 publications

The biomechanics of running and running styles: a synthesis

The biomechanics of running and running styles: a synthesis

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Reliability of Trail Walking and Running Tasks Using the Stryd Power Meter

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