Human Gait Symmetry Assessment using a Depth Camera and Mirrors

TL;DR

This paper introduces a novel method for assessing human gait symmetry using a depth camera and mirrors, leveraging 3D point clouds and histogram analysis to quantify gait symmetry indices.

Contribution

It presents a new approach combining depth sensing and mirror setup for reliable gait symmetry assessment, with a novel histogram-based analysis method.

Findings

Effective in assessing gait symmetry across 9 gait types

Outperforms related methods with different input data

Provides quantitative gait symmetry indices

Abstract

This paper proposes a reliable approach for human gait symmetry assessment using a depth camera and two mirrors. The input of our system is a sequence of 3D point clouds which are formed from a setup including a Time-of-Flight (ToF) depth camera and two mirrors. A cylindrical histogram is estimated for describing the posture in each point cloud. The sequence of such histograms is then separated into two sequences of sub-histograms representing two half-bodies. A cross-correlation technique is finally applied to provide values describing gait symmetry indices. The evaluation was performed on 9 different gait types to demonstrate the ability of our approach in assessing gait symmetry. A comparison between our system and related methods, that employ different input data types, is also provided.