Marker-free Human Gait Analysis using a Smart Edge Sensor System
This work addresses the need for cost-effective and reliable gait analysis for biomechanics and medical experts, though it appears incremental as it builds on existing markerless and sensor-based methods.
The paper tackled the problem of expensive and time-consuming gait analysis by introducing a markerless approach using a multi-camera setup with smart edge sensors to estimate 3D body poses, and it demonstrated the potential for efficient automated analysis in real-world environments.
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.