Visualising gait symmetry/asymmetry from acceleration data

Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation.