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

July 21, 2025 · View on GitHub

This is the official repository for the paper "ProGait: A Multi-Purpose Video Dataset and Benchmark for Transfemoral Prosthesis Users" (ICCV'25).

🆕Updates

  • 2025/7/2 We have published our dataset at Hugging Face: Link to the dataset page.
  • 2025/6/25 Our paper "ProGait: A Multi-Purpose Video Dataset and Benchmark for Transfemoral Prosthesis Users" get accepted by ICCV'25! :tada: :tada: :tada:

Table of Content

What is ProGait

ProGait is a multi-purpose video dataset aimed to support multiple vision tasks on prosthesis users, including Video Object Segmentation, 2D Human Pose Estimation, and Gait Analysis. 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.

Example annotations:

ProGait Dataset

Download

  • The raw videos and corresponding annotations is available HERE.
  • Instructions for downloading can be found HERE. Use huggingface-cli for example: 
    huggingface-cli download ericyxy98/ProGait --repo-type dataset --local-dir path/to/dataset
rm -r path/to/dataset/.cache

Annotation format

annotations
├── inside    <------------------ Scenario: inside parallel bars
│   ├── *_annotations.xml  <----- CVAT XML format
│   ├── *_keypoints.npy.gz  <---- 2D pose keypoints in format of numpy ndarray
│   ├── *_masks.npy.gz  <-------- Segmentation masks in format of numpy ndarray
│   └── *.txt  <----------------- Textual descriptions
└── outside   <------------------ Scenario: outside parallel bars
    └── ...
  • The IDs are named in format of <subject>_<prosthesis>_<trial>_<f(rontal)/s(agittal)>[_<additional round trips>]. For example: 1_3_2_f refers to the frontal view of Subject 1 using their 3rd prosthesis and having their 2nd walking trial; 2_6_2_s_2 refers to the sagittal view of Subject 2 using their 6th prosthesis and having their 2nd walking trial, specifically the 2nd additional round-trip (which is the 3rd overall).
  • Pose keypoints have dimensions of (num_frames, num_keypoints, 3), where the 3 corresponds to x-, y-coordinates, and confidence scores.
  • Segmentation masks have dimensions of (num_frames, frame_height, frame_width, 1).
  • NOTE: A single text description can apply to multiple video sequences within the same walking trial.

Usage

  1. Clone the repository 
    git clone https://github.com/pittisl/ProGait.git
cd ProGait
  2. Setup the virtual environment 
    conda env create -f environment.yml
conda activate progait
  3. Prepare the dataset
    • Download the dataset. See above.
    • Place the data files under datasets/progait/, which should look like: 
      .
├── datasets
│   └── progait
│       ├── annotations
│       |   └── ...
│       ├── previews
│       |   └── ...
│       ├── videos
│       |   └── ...
│       └── metadata.jsonl
├── scripts
│   └── ...
├── models
│   └── ...
├── README.md
└── ...
  4. Run the demo 
    python verify_data.py

Baseline models

  • TBD

Tasks

ProGait provides annotations for 3 different tasks:

Video Object Segmentation (VOS)

  • Bounding boxes and segmentation masks of the prothesis user

2D Human Pose Estimation (HPE)

  • 23 pose keypoints of the target (17 for body and 6 for feet, following the COCO-wholebody definition)

Gait Analysis

  • Text descriptions of four key components:
    • The general gait category
    • The specific gait deviation
    • Recommendations on how to adjust the prosthesis to correct the gait
    • The reasons of these recommendations

Evaluation

Terms of Use

This project is released under the MIT License.

Disclaimer

We are aware that Orthocare Innovations PLLC also used "ProGait" as the name of their mobile app product. Our work and dataset are not affiliated with Orthocare Innovations PLLC, and are not associated with their ProGait app, Europa+ system, or any other product.

How to cite

If you find ProGait dataset useful for your project, please cite our paper as follows.

Xiangyu Yin, Boyuan Yang, Weichen Liu, Qiyao Xue, Abrar Alamri, Goeran Fiedler, Wei Gao, "ProGait: A Multi-Purpose Video Dataset and Benchmark for Transfemoral Prosthesis Users", ICCV, 2025.

BibTeX entry:

TBD