A comparison of kinematic algorithms to estimate gait events during overground running

Abstract

The gait cycle is frequently divided into two distinct phases, stance and swing, which can be accurately determined from ground reaction force data. In the absence of such data, kinematic algorithms can be used to estimate footstrike and toe-off. The performance of previously published algorithms is not consistent between studies. Furthermore, previous algorithms have not been tested at higher running speeds nor used to estimate ground contact times. Therefore the purpose of this study was to both develop a new, custom-designed, event detection algorithm and compare its performance with four previously tested algorithms at higher running speeds. Kinematic and force data were collected on twenty runners during overground running at 5.6m/s. The five algorithms were then implemented and estimated times for footstrike, toe-off and contact time were compared to ground reaction force data. There were large differences in the performance of each algorithm. The custom-designed algorithm provided the most accurate estimation of footstrike (True Error 1.2 ± 17.1 ms) and contact time (True Error 3.5 ± 18.2 ms). Compared to the other tested algorithms, the custom-designed algorithm provided an accurate estimation of footstrike and toe-off across different footstrike patterns. The custom-designed algorithm provides a simple but effective method to accurately estimate footstrike, toe-off and contact time from kinematic data.

A kinematic algorithm to identify gait events during running at different speeds and with different footstrike types

“…It also performed as well as previous studies based on a single running speed (Smith et al, 2015) or an unspecified range of speeds (Hreljac & Stergiou, 2000). The range of GCTs observed in this study are likely to correspond to running speeds of approximately 2-6 m·s -1 (Forrester and Townend, 2015).…”

“…The accuracy of results for the Toe vertical jerk peak algorithm are as good or better than those reported for the Toe vertical acceleration peak algorithm in these previous studies. The accuracy is also as good or better than previous algorithms based on the use of alternative lower limb based variables to determine toe-off (Fellin et al, 2010;Hreljac & Stergiou, 2000;Smith et al, 2015).…”

“…Consequently, a number of kinematics-based algorithms to detect GCEs have been developed. Several authors have proposed algorithms for walking (De Witt et al, 2010;O'Connor et al, 2007;Zeni et al, 2008), and for running using only foot-based markers (Leitch et al 2011;Maiwald et al, 2009), only markers on more proximal segments (Milner et al, 2015), as well as both foot-based and more proximal segment markers (Fellin et al, 2010;Hreljac & Stergiou, 2000;Smith et al 2015). One algorithm has been proposed for both walking and running, although the running speeds were relatively low (2.5-3.6 m•s -1 ) (Alvim et al, 2015).…”

“…One algorithm has been proposed for both walking and running, although the running speeds were relatively low (2.5-3.6 m•s -1 ) (Alvim et al, 2015). Existing running algorithms have typically been limited to either only rearfoot strike running (Alvim et al, 2015;Hreljac & Stergiou, 2000) and / or single speed or multiple slow running speeds (Alvim et al, 2015;Fellin et al, 2010;Milner et al, 2015;Smith et al, 2015) and few have reported the accuracy of ground contact time (GCT) as well as the individual touchdown and toe-off GCEs (Hreljac & Stergiou, 2000;Smith et al, 2015).…”

A kinematic method to detect foot contact during running for all foot strike patterns

“…This extra step would necessitate either collection of video data or pre-evaluation of kinematic data. Several kinematic methods for determining foot contact have been compared in a group of runners with rearfoot, midfoot, and forefoot strike patterns (Smith et al, 2015). Unfortunately, different error magnitudes were found for the best algorithm among strike patterns, which may hinder its use with non-rearfoot strikers.…”