Kavli Affiliate: Wei Gao
| First 5 Authors: Xiangyu Yin, Xiangyu Yin, , ,
| Summary:
Prosthetic legs play a pivotal role in clinical rehabilitation, allowing
individuals with lower-limb amputations the ability to regain mobility and
improve their quality of life. Gait analysis is fundamental for optimizing
prosthesis design and alignment, directly impacting the mobility and life
quality of individuals with lower-limb amputations. Vision-based machine
learning (ML) methods offer a scalable and non-invasive solution to gait
analysis, but face challenges in correctly detecting and analyzing prosthesis,
due to their unique appearances and new movement patterns. In this paper, we
aim to bridge this gap by introducing a multi-purpose dataset, namely ProGait,
to support multiple vision tasks including Video Object Segmentation, 2D Human
Pose Estimation, and Gait Analysis (GA). ProGait provides 412 video clips from
four above-knee amputees when testing multiple newly-fitted prosthetic legs
through walking trials, and depicts the presence, contours, poses, and gait
patterns of human subjects with transfemoral prosthetic legs. Alongside the
dataset itself, we also present benchmark tasks and fine-tuned baseline models
to illustrate the practical application and performance of the ProGait dataset.
We compared our baseline models against pre-trained vision models,
demonstrating improved generalizability when applying the ProGait dataset for
prosthesis-specific tasks. Our code is available at
https://github.com/pittisl/ProGait and dataset at
https://huggingface.co/datasets/ericyxy98/ProGait.
| Search Query: ArXiv Query: search_query=au:”Wei Gao”&id_list=&start=0&max_results=3