A Single RGB Camera Based Gait Analysis with a Mobile Tele-Robot for Healthcare

TL;DR

This paper presents a novel, low-cost, marker-less gait analysis system using a single RGB camera mounted on a mobile robot, employing pose estimation and machine learning to classify walking patterns for healthcare.

Contribution

It introduces a new gait analysis approach with a single RGB camera and pose estimation models, enabling low-resource, real-time health monitoring in home environments.

Findings

Achieves competitive accuracy with depth and multi-camera systems

Operates efficiently on low-resource hardware

Successfully classifies different gait patterns

Abstract

With the increasing awareness of high-quality life, there is a growing need for health monitoring devices running robust algorithms in home environment. Health monitoring technologies enable real-time analysis of users' health status, offering long-term healthcare support and reducing hospitalization time. The purpose of this work is twofold, the software focuses on the analysis of gait, which is widely adopted for joint correction and assessing any lower limb or spinal problem. On the hardware side, we design a novel marker-less gait analysis device using a low-cost RGB camera mounted on a mobile tele-robot. As gait analysis with a single camera is much more challenging compared to previous works utilizing multi-cameras, a RGB-D camera or wearable sensors, we propose using vision-based human pose estimation approaches. More specifically, based on the output of two state-of-the-art human pose estimation models (Openpose and VNect), we devise measurements for four bespoke gait parameters: inversion/eversion, dorsiflexion/plantarflexion, ankle and foot progression angles. We thereby classify walking patterns into normal, supination, pronation and limp. We also illustrate how to run the purposed machine learning models in low-resource environments such as a single entry-level CPU. Experiments show that our single RGB camera method achieves competitive performance compared to state-of-the-art methods based on depth cameras or multi-camera motion capture system, at smaller hardware costs.