Human Dynamics Estimation (HDE)

Human Dynamics Estimation (HDE) is a collection of YARP devices for the online estimation of the kinematics and dynamics of a human subject monitored with a set of wearable sensors and/or interacting with a robot. A ROS-based visualizer allows to visualize in real-time the output of the estimation. The devices can be installed and run in Linux, macOS and Windows.

!NOTE Since September 2024 (release 4.0.0) the human-dynamics-estimation repo contains the software that used to be part of the https://github.com/robotology/wearables repository.

Contents

• Overview
• Dependencies
• How to install
• Applications
• Citing this work
• Acknowledgments

Overview

The main devices contained in this project are the following:

• HumanStateProvider: solve inverse kinematics given a set of kinematic sensor.
• HumanDynamicsEstimator: solve the inverse dynamics given the kinematic state and a set of wrenches measurment.
• RobotPositionControl: controls the position of a robot with a given kinematic state.

The information coming from the sensors come in the form of the IWear [YARP interface](https://www.yarp.it/group__dev__iface.html, contained in the interfaces/IWear folder. All the information exchanged among the HDE devices trough implmented in this project (IHumanDynamics, IHumanState, IHumanWrench) and can be published among a network using the coreresponding wrapper devices

A possible architecture integrating wearable sensors and HDE is described in the following scheme:

Dependencies

Here following there is a list of dependencies you need for using this repository. It is worth to notice that the build ones and the libraries are mandatory to install your project. Instead, the optional dependencies are defined optional in the sense that the project is built even if they are not included. The installation of the all dependencies is strongly suggested if you want to have a visual feedback of how much your estimation is good.

For installing the dependencies you can decide to install them individually or to use the robotology-superbuild with the ROBOTOLOGY_ENABLE_DYNAMICS option that automatically is in charge of installing all the dependencies you need (except for the optional ones). Keep in mind that the robotology-superbuild is surely the fastest way to install them but it contains many more things than you need!

Build dependencies

• CMake: an open-source, cross-platform family of tools designed to build, test and package software.
• YCM: a CMake project whose only goal is to download and build several other projects.

Libraries

• YARP: a library and toolkit for communication and device interfaces.
• icub-main: a library for the interaction with the iCub robot.
• iDynTree: a library of robots dynamics algorithms for control, estimation and simulation.
• Eigen (3.3 or later): a C++ template library for linear algebra.
• IPOPT: a software package for large-scale nonlinear optimization.

Optional dependencies

• ROS with rviz package: an open-source provider of libraries and tools for creating robot applications.
• irrlicht and iDynTree compiled with IDYNTREE_USES_IRRLICHT enabled: visualizer for floating-base rigid-body systems.
• Robometry : a telemetry suite for logging data.
• pybind11: if present, the user can enable HDE_COMPILE_PYTHON_BINDINGS to enable the compilation of the python bindings.

How to install

After installing all the dependencies, you can install the HDE project:

git clone https://github.com/robotology/human-dynamics-estimation.git
mkdir build
cd build

cmake -DCMAKE_INSTALL_PREFIX=/path/to/your/installation/folder -G "name-of-your-cmake-generator" ..

where the name-of-your-cmake-generator is your project generator, see Cmake-Generators. For example, on macOS you may choose Xcode, or on Unix Unix Makefiles. You can configure the following optional cmake options:

• HUMANSTATEPROVIDER_ENABLE_VISUALIZER: enables the irricht-based iDynTree Visualizer.
• HUMANSTATEPROVIDER_ENABLE_LOGGER: enables the robometry-based data logger.
• ENABLE_FrameVisualizer: enable the compilation of the IWearFrameVisualizer module that allows to visualize wearble inertial measurements.
• ENABLE_Logger: enable the compilation of the IWearLogger device that allows to save wearable data as .mat or stream the data on YARP as analog vector data.
• ENABLE_<device>: enable the compilation of the optional wearable device (see documentation in Wearable device sources).

Then, for compiling

cmake --build . --config Release

and installing

cmake --build . --config Release --target install

Wearable device sources

Wearable devices contained in /devices/ are YARP devices that exposes sensors data using IWear interface. List of the dependencies and documentation for running some of the wearable data sources can be found at the links in the table below. The compilation of some of theese devices can be enable/disabled changing the CMAKE option ENABLE_<device>.

Device Name	Description	OS	Dependencies	Documentation
IAnalogSensor	Exposes YARP IAnalogSensor Interface.	Linux/MacOS/Windows	-	-
FTShoes	Exposes YARP ftShoe data.	Linux/MacOS/Windows	forcetorque-yarp-devices	📚
XsensSuit	Exposes XsensSuit data.	Windows	xsens MVN SDK 2018.0.3	📚
ICub	Exposes iCub robot data.	Linux/MacOS/Windows	iDynTree	📚
Paexo	Exposes Paexo data.	Linux	iFeelDriver (Contact the maintainer for more details)	-
HapticGlove	Exposes SenseGlove data.	Linux/Windows	SenseGloveSDK	📚
IFrameTransform	Exposes YARP IFrameTransform Interface.	Linux/MacOS/Windows	-	-

Applications

The code contained in this repository can serve different application. Depending on the type of application, a different set of hardware and sensors are required. In addiction, since the applications are based on wearables devices, a certain number of wearable devices should be running providing the source sensor data. The main applications are the following

Application	hardware	werable device
Inverse Kinematics	kinematic sensors	XsensSuit
Dynamics Estimation	kinematic sensors FTshoes	XsensSuit FTshoes
Whole-Body Retargeting	kinematic sensors	XsensSuit

How to run

Applications

Documentation for running each specific application can be found at the following links:

• Inverse Kinematics
• Dynamics Estimation
• Whole-Body Retargeting

Citing this work

Please cite the following publications if you are using the code contained in this repository for your own research and/or experiments:

MDPI Sensors

Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques.
Latella, C., Traversaro, S., Ferigo, D., Tirupachuri, Y., Rapetti, L., Andrade Chavez, F. J., Nori F., Pucci, D.
Sensors, 19(12), 2794., 2019, doi:
10.3390/s19122794
https://www.mdpi.com/1424-8220/19/12/2794

The bibtex code for including this citation is provided:

@article{latella2019simultaneous,
  title={Simultaneous floating-base estimation of human kinematics and joint torques},
  author={Latella, Claudia and Traversaro, Silvio and Ferigo, Diego and Tirupachuri, Yeshasvi and Rapetti, Lorenzo and Andrade Chavez, Francisco Javier and Nori, Francesco and Pucci, Daniele},
  journal={Sensors},
  volume={19},
  number={12},
  pages={2794},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}

IEEE Robotics and Automation Letters

The CoDyCo Project achievements and beyond: Towards Human Aware Whole-body Controllers for Physical Human Robot Interaction
Francesco Romano, Gabriele Nava, Morteza Azad, Jernej Camernik, Stefano Dafarra, Oriane Dermy, Claudia Latella, Maria Lazzaroni, Ryan Lober, Marta Lorenzini, Daniele Pucci, Olivier Sigaud, Silvio Traversaro, Jan Babic, Serena Ivaldi, Michael Mistry, Vincent Padois, Francesco Nori
IEEE Robotics and Automation Letters
DOI: 10.1109/LRA.2017.2768126
http://ieeexplore.ieee.org/document/8093992

The bibtex code for including this citation is provided:

@article{romano2017codyco,
  title={The CoDyCo Project achievements and beyond: Towards Human Aware Whole-body Controllers for Physical Human Robot Interaction},
  author={Romano, Francesco and Nava, Gabriele and Azad, Morteza and Camernik, Jernej and Dafarra, Stefano and Dermy, Oriane and Latella, Claudia and Lazzaroni, Maria and Lober, Ryan and Lorenzini, Marta and others},
  year={2017},
  DOI={10.1109/LRA.2017.2768126},
  publisher={IEEE}, 
  journal={IEEE Robotics and Automation Letters}, 
  url={http://ieeexplore.ieee.org/document/8093992}, 
}

Acknowledgments

The development of HDE is supported by the FP7 EU projects CoDyCo (No. 600716 ICT 2011.2.1 Cognitive Systems and Robotics) and by H2020 EU projects An.Dy (No. 731540 H2020-ICT-2016-1). The development is also supported by the Istituto Italiano di Tecnologia.

Mantainers

• Silvio Traversaro (@traversaro)
• Stefano Dafarra (@S-Dafarra)
• Dario Sortino (@dariosortino)