Gait events detection from heel and toe trajectories: comparison of methods using multiple datasets

Abstract

Reliable detection of gait events is important to ensure accurate assessment of gait. While it is usually performed resorting to force platforms, methods based uniquely on kinematic analysis have also been proposed. These methods place no restrictions on the number of steps that can be analysed, simplifying setup and complexity of assessments. They also replace the need of annotating events manually when force platforms are not available. Although few methods have been proposed in literature, validation studies are relatively scarce. In this study we present multiple methods for the detection of heel strike (HS) and toe off (TO) in normal walking, and validate the detection against annotated events using three different datasets. The best performing candidates are based on the evaluation of heel vertical velocity (for HS) and toe vertical acceleration (for TO), resulting in relative errors of -12.4 ± 32.9 ms for HS and of -15.5 ± 24.9 ms for TO. The method is compatible with barefoot and shod walking, constituting a convenient, fast and reliable alternative to automatic gait event detection using kinematic data.