Human Inertial Pose

Human whole-body pose estimation using inertial sensor data.

demo_video.mp4

(Demo video: (Left) Xsens-Analyse GT; (Right) MTwAwinda MARG sensor data calibrated and fused using this library.)

Contains code to

• Calculate sensors orientation (based on ahrs) library.
• Perform sensor-to-segment calibration.
• Perform imu sensor calibration.
• Compute evaluation metrics.
• Define and compute skeleton kinematics (get joint 3D positions and 3D visualization).
• Mapping between some skeleton formats (Xsens(23) / MTwAwinda(17) / XsensUpper(15) / MTwAwindaUpper(11) Ergowear(9))
• Handle some common external exported data (Xsens-Analyse, Xsens-MtManager).
• Parse popular inertial pose datasets (WIP).

---

Usage

from hipose.data.trial_parsing.extract_xsens_mtmanager import extract_mtmanager_raw_data
example_data_path = "./resources/sample_trials/xsens2mtmanager/example_calibration_xsens/"
imu_data = extract_mtmanager_raw_data(example_data_path)

# initialize filter fusion (example trial has 17 IMUs)
from hipose.api.fusion_filter import InertialPoseFusionFilter
ffilts = InertialPoseFusionFilter(
        num_imus=17, 
        ignore_mag=False,
        fusion_filter_alg="madgwick",
        s2s_calib_method="static_mag",
        default_data_freq=imu_data["freq"],
)

# initialize calibration params from static NPose
# (example trial has 5s of NPose at the start)
calib_s = int(imu_data["freq"] * 5)
ffilts.compute_imus_calibration(acc_calib_data=imu_data["acc"][0:calib_s],
                                gyr_calib_data=imu_data["gyr"][0:calib_s],
                                mag_calib_data=imu_data["mag"][0:calib_s])

# perform filter fusion on trial data to obtain segment orientations
for idx, (acc, gyr, mag) in enumerate(zip(imu_data["acc"][calib_s:],
                                          imu_data["gyr"][calib_s:],
                                          imu_data["mag"][calib_s:])):
    pred_ori = ffilts.update(acc=acc, gyr=gyr, mag=mag)

Look at /examples/example_visualize_evaluate_inertial_pose.py for a more detailed example showing additional features (e.g. 3D skeleton display, skeleton mapping, metrics calculation, etc...).

---

Installation

Minimal Installation

pip install hipose                                # install package with base requirements

Complete Installation

pip install hipose"[parse,plot,vis3d,test,doc]"   # install package with desired extra dependencies
# pip install hipose"[all]"                       # or install package with all extra dependencies

TODOs

(Pull Request are welcome!)

• Add parsing utils for commonly used inertial pose estimation datasets in the literature.
  • DIP_IMU.
  • TotalCapture.
• Improve dynamic optimization s2s calibration method.
• Rewrite skeleton implementation + improve functionality.
• Improve unittest coverage.
• Improve Docs.

---

Development

Install latest repository version

git clone https://github.com/ManuelPalermo/HumanInertialPose.git
cd HumanInertialPose/
pip install -e .                                                        # install package with base requirements
# pip install -e ."[parse,plot,vis3d,test,doc]"  # or ."[all]"          # install package with extra dependencies

Running Unittests

Unittests should ideally be added to cover all implemented code and should be run before submitting any commit! Running them requires additional dependencies which can be installed with: pip install -e ."[test]"

Tests can be run by calling (from the project root) one of:

sh test/test.sh               # run all unittests
# sh test/test_complete.sh    # run all unittests + code quality + docs quality

Building docs

Building the api documentation requires additional dependencies which can be installed with: pip install -e ."[doc]"

The docs can be compiled by calling (from the project root):

cd docs
make html

Common Problems / Fixes

3D Visualizer

• "libGL error: MESA-LOADER: failed to open swrast : .../lib/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /usr/lib/dri/swrast_dri.so) (search paths /usr/lib/dri)": https://unix.stackexchange.com/questions/655495/trying-to-run-pygame-on-my-conda-environment-on-my-fresh-manjaro-install-and-ge

---

Acknowledgements

This project was developed from a research scholarship at the BirdLab-UMinho, supported by grant POCI-01-0247-FEDER-39479. You can check out other works being developed there through the lab's (Github / Instagram / Facebook).

A special thanks to the people in the Ergoaware project who also contributed to the library through helpful discussions, data acquisition and testing.

Citation

If you find the project helpful, please consider citing us (temporary citations):

@misc{palermo2022complete,
    author      = {Manuel Palermo and Sara Cerqueira and João André and António Pereira and Cristina P. Santos},
    title       = {Complete Inertial Pose Dataset: from raw measurements to pose with low-cost and high-end MARG sensors},
    year        = {2022},
    eprint      = {2202.06164},
    eprinttype  = {arXiv},
    url         = {https://arxiv.org/abs/2202.06164}
}

@dataset{palermo2022cipdatabase,
    author       = {Manuel Palermo and Sara Cerqueira and João André and António Pereira and Cristina P. Santos},
    title        = {Complete Inertial Pose (CIP) Dataset},
    year         = {2022},
    publisher    = {Zenodo},
    doi          = {10.5281/zenodo.5801928},
    url          = {https://doi.org/10.5281/zenodo.5801928}
}