ProGait: A Multi-Purpose Video Dataset and Benchmark for Transfemoral Prosthesis Users

TL;DR

ProGait introduces a comprehensive video dataset and benchmark for analyzing gait and prosthesis use in transfemoral amputees, supporting multiple vision tasks to improve prosthetic design and rehabilitation.

Contribution

The paper presents a novel multi-purpose dataset, ProGait, with benchmark tasks and baseline models tailored for prosthesis-specific gait analysis using vision-based ML methods.

Findings

Baseline models outperform pre-trained models on prosthesis tasks.

ProGait improves generalizability of vision models for prosthesis analysis.

Dataset supports multiple vision tasks like segmentation, pose estimation, and gait analysis.

Abstract

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.