Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users

Autor:	Zhou, Tianxun, Iskandar, Muhammad Nur Shahril, Chiam, Keng-Hwee
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The application of 2D markerless gait analysis has garnered increasing interest and application within clinical settings. However, its effectiveness in the realm of lower-limb amputees has remained less than optimal. In response, this study introduces an innovative zero-shot method employing image generation diffusion models to achieve markerless pose estimation for lower-limb prosthetics, presenting a promising solution to gait analysis for this specific population. Our approach demonstrates an enhancement in detecting key points on prosthetic limbs over existing methods, and enables clinicians to gain invaluable insights into the kinematics of lower-limb amputees across the gait cycle. The outcomes obtained not only serve as a proof-of-concept for the feasibility of this zero-shot approach but also underscore its potential in advancing rehabilitation through gait analysis for this unique population. Comment: 25 pages, 6 figures. Supplementary documents in source file
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.07854 Zobrazit plný text záznamu View this record from Arxiv