Marker-free Human Gait Analysis using a Smart Edge Sensor System

TL;DR

This paper presents a novel markerless gait analysis system using smart edge sensors and a Siamese network to accurately identify individuals and analyze gait without the need for markers, offering a cost-effective and reliable alternative to traditional methods.

Contribution

The paper introduces a new markerless gait analysis approach with a multi-camera smart sensor system and a Siamese embedding network for individual identification.

Findings

Effective gait-based individual identification demonstrated

System operates reliably in real-world environments

Potential for automated, cost-effective gait analysis

Abstract

The human gait is a complex interplay between the neuronal and the muscular systems, reflecting an individual's neurological and physiological condition. This makes gait analysis a valuable tool for biomechanics and medical experts. Traditional observational gait analysis is cost-effective but lacks reliability and accuracy, while instrumented gait analysis, particularly using marker-based optical systems, provides accurate data but is expensive and time-consuming. In this paper, we introduce a novel markerless approach for gait analysis using a multi-camera setup with smart edge sensors to estimate 3D body poses without fiducial markers. We propose a Siamese embedding network with triplet loss calculation to identify individuals by their gait pattern. This network effectively maps gait sequences to an embedding space that enables clustering sequences from the same individual or activity closely together while separating those of different ones. Our results demonstrate the potential of the proposed system for efficient automated gait analysis in diverse real-world environments, facilitating a wide range of applications.