Elsevier

Gait & Posture

Volume 35, Issue 2, February 2012, Pages 298-300
Gait & Posture

Full length article
A kinematic method for footstrike pattern detection in barefoot and shod runners

https://doi.org/10.1016/j.gaitpost.2011.09.104Get rights and content

Abstract

Footstrike patterns during running can be classified discretely into a rearfoot strike, midfoot strike and forefoot strike by visual observation. However, the footstrike pattern can also be classified on a continuum, ranging from 0% to 100% (extreme rearfoot to extreme forefoot) using the strike index, a measure requiring force plate data. When force data are not available, an alternative method to quantify the strike pattern must be used. The purpose of this paper was to quantify the continuum of foot strike patterns using an easily attainable kinematic measure, and compare it to the strike index measure. Force and kinematic data from twenty subjects were collected as they ran across an embedded force plate. Strike index and the footstrike angle were identified for the four running conditions of rearfoot strike, midfoot strike and forefoot strike, as well as barefoot. The footstrike angle was calculated as the angle of the foot with respect to the ground in the sagittal plane. Results indicated that the footstrike angle was significantly correlated with strike index. The linear regression model suggested that strike index can be accurately estimated, in both barefoot and shod conditions, in the absence of force data.

Introduction

There has been a growing interest in the mechanics associated with different footstrike patterns (FSP) during running, especially in light of the suggested relationship between FSP and injury [1], [2], [3]. FSPs can be classified into three groups. A rearfoot strike (RFS) is one where the heel hits the ground first, a midfoot strike (MFS) is one where the foot lands flat on the ground and forefoot strike (FFS) is where the ball of the foot first strikes the ground. It has been reported that about 75% of all shod runners are RFS, 24% are MFS, and the remaining 1% are FFS [4], [5]. However, barefoot running is almost always associated with a FFS or MFS pattern [6], [7].

While FSP can be visually classified into one of the three types, it is quantified by the strike index (SI), using a force plate [8]. This is a measure of the location, at initial contact, of the center of pressure (COP) along the long axis of the foot as a percentage of the total foot length. The calculation is detailed in Fig. 1. A SI of 0–33% indicates a RFS, 34–67% a MFS, and 68–100% a FFS. Unfortunately, force data are not always available, as in the natural running environment, or when running on a conventional treadmill. The advent of instrumented treadmills does allow the calculation of SI during continuous running. However, these force treadmills inherently introduce more noise to the force data than those force plates that are floor-embedded. This noise adds significant error to the COP calculation, and is especially troublesome when forces are low at initial contact. However, the location of the COP should be related to the orientation of the foot at contact, with a more dorsiflexed position associated with a lower SI. As a result, the angle of the foot segment in the sagittal plane at footstrike may be provide a surrogate measure of SI.

Therefore, the purpose of this study was to determine whether SI can be estimated in the absence of force data, using the kinematic measure of the footstrike angle (FSA) of the foot. It was hypothesized that as FSA increased (towards more of a rearfoot strike) SI would decrease in both barefoot and shod runners across a range of FSP.

Section snippets

Methods

Twenty healthy runners (10 female, 10 male, aged 27.8 ± 8.9 yr, running 20.7 ± 13.0 min/week) with various natural FSPs were recruited from the University of Delaware and surrounding community. Each subject had markers placed on their right foot (Fig. 2). After subject calibration, anatomical markers were removed. Subjects ran across a force plate (Bertec, Columbus, OH) until five trials were collected at 1000 Hz for each condition. Each subject ran utilizing RFS, MFS, FFS and barefoot (BFS)

Results

In the shod condition, FSA was strongly correlated with SI, R = 0.92 (p < 0.01). The linear regression model with FSA is shown in Eq. (1) and Fig. 3.FSA=0.39×SI+27.4,R2=0.85

FSA was again strongly correlated with SI in the barefoot condition (R = 0.86, p < 0.01). The linear regression model is shown in Eq. (2) and Fig. 3.FSA=0.32×SI+23.1,R2=0.74

Six shod and three barefoot footstrikes were classified as MFS by the SI. These trials were used to determine criteria for FSP classification. The mean FSA

Discussion

The purpose of this study was to determine whether FSA could serve as a surrogate for the SI, an established measure of FSP. FSA was found to be a strong predictor of SI for both the shod and barefoot conditions. The FSA data appear to be appropriately partitioned into the correct ranges of SI, as the MFS are centered about 0°, corresponding to foot-flat, and RFS and FFS are in good agreement with the SI and visual determination. This is especially true for the shod running. The explained

Conclusion

FSA was significantly correlated with SI in both barefoot and shod conditions. These data suggest that FSA is an acceptable measure of FSP when force data are not available for both barefoot and shod runners.

Conflict of interest

Study sponsors were not involved in the study design, collection, analysis and interpretation of data, writing of the manuscripts or the decision to submit the manuscript for publication.

Acknowledgements

Funding: Drayer Physical Therapy Institute, DOD W911NF-05-1-0097, and NIH 1 S10 RR022396.

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