Zhou Tianxun, Iskandar Muhammad Nur Shahril, Chiam Keng-Hwee
Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore.
Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore.
PLOS Digit Health. 2025 Mar 31;4(3):e0000745. doi: 10.1371/journal.pdig.0000745. eCollection 2025 Mar.
Quantitative gait analysis is important for assessing and rehabilitating lower-limb prosthetic users, but markerless motion capture has been challenging for this population due to the difficulty in detecting prosthetic joints using models trained primarily on able-bodied individuals. This study proposes a zero-shot method leveraging generative diffusion models to transform prosthetic limb images into able-bodied representations that standard pose estimation models can detect, eliminating the need for additional data collection or model retraining. Videos of unilateral transfemoral and transtibial amputees walking were obtained publicly from YouTube. For each video frame, an edge map was generated and used as input to a ControlNet diffusion model, generating a synthetic image resembling an able-bodied person while preserving the person's original pose. These synthetic images were then passed through OpenPose. The zero-shot approach achieved substantial reductions in keypoint coordinate errors of 37% for transtibial and 76% for transfemoral prosthetic limbs compared to OpenPose on the original videos. The method enabled the identification and quantification of key gait deviations such as reduced knee flexion and altered kinematics timing between prosthetic and intact limbs. While the results demonstrate the feasibility of markerless gait analysis for lower-limb prosthetic users, the study's findings are based on a limited dataset of publicly available videos, and caution should be exercised in generalizing the results to broader populations due to the varying nature of prosthetic designs. Nonetheless, this approach has the potential to facilitate personalized rehabilitation using standard consumer cameras and existing pose estimation models.
定量步态分析对于评估和康复下肢假肢使用者很重要，但由于使用主要基于健全个体训练的模型来检测假肢关节存在困难，无标记运动捕捉对于这一人群一直具有挑战性。本研究提出了一种零样本方法，利用生成扩散模型将假肢图像转换为标准姿态估计模型能够检测的健全人表示形式，从而无需额外的数据收集或模型再训练。从YouTube上公开获取了单侧股骨截肢和胫骨截肢者行走的视频。对于每个视频帧，生成一个边缘图并将其用作ControlNet扩散模型的输入，生成一个类似于健全人的合成图像，同时保留人物的原始姿态。然后将这些合成图像通过OpenPose。与在原始视频上使用OpenPose相比，这种零样本方法使胫骨假肢的关键点坐标误差大幅降低了37%，股骨假肢的关键点坐标误差大幅降低了76%。该方法能够识别和量化关键的步态偏差，如膝关节屈曲减少以及假肢与健全肢体之间运动学时间的改变。虽然结果证明了无标记步态分析对于下肢假肢使用者的可行性，但该研究的发现基于一个有限的公开可用视频数据集，并且由于假肢设计的多样性，在将结果推广到更广泛人群时应谨慎。尽管如此，这种方法有潜力利用标准的消费级相机和现有的姿态估计模型来促进个性化康复。
