Arm Swing Asymmetry Measurement from 2D Gait Videos

Abstract

Arm swing during gait has been positively related to gait stability and gait efficiency, particularly in the presence of neurological disorders that affect locomotion. However, most gait studies have focused on lower extremities, while arm swing usually remains ignored. In addition, these studies are mostly based on costly, highly-specialized vision systems or on wearable devices which, despite their popularity among researchers and specialists, are still relatively uncommon for the general population. This work proposes a way of estimating arm swing asymmetry from a single 2D gait video. First, two silhouette-based representations that separately capture motion data from both arms were built. Second, a measure to quantify arm swing energy from such a representation was introduced, producing two side-dependent motion measurements. Third, an arm swing asymmetry index was obtained. The method was validated on two public datasets, one with 68 healthy subjects walking normally and one with 10 healthy subjects simulating different styles of arm swing asymmetry. The validity of the asymmetry index at capturing different arm swing patterns was assessed by two non-parametric tests: the Mann–Whitney U test and the Wilcoxon signed-rank test. The so-called physiological asymmetry was observed on the normal gait sequences of both datasets in a statistically similar way. The asymmetry index was able to fairly characterize the different levels of asymmetry simulated in the second set. Results show that it is possible to estimate the arm swing asymmetry from a single 2D gait video, with enough sensitivity to discriminate anomalous patterns from normality. This opens the door to low-cost easy-to-use mobile applications to assist clinicians in monitoring gait condition in primary care (e.g., in the elderly), when more accurate and specialized technologies are often not available.