MAPest (Maximun-A-Posteriori estimation) is a probabilistic tool for estimating dynamics variables in humans.
The code in this repository works only off-line and to solve the problem of the estimation of human dynamics you need to have a dataset of different measurements coming from sensors. For the moment the repository consists of two type of experiments:
- 1st experiment (2dofBowingTask): where we consider a very simplified human model with 3 link and 2 Dofs and a sensor architecture composed by the Vicon motion capture + an IMU + one forceplate;
- 2nd experiment (23links_human): where the model is composed by 23 links and for the sensor structure by the Xsens suit + two sensorized shoes.
Detailes on the experiments in the README of each related Section.
Please cite the following publication if you are using the code contained in this repository for your own research and/or experiments:
Latella, C.; Lorenzini, M.; Lazzaroni, M.; Romano, F.; Traversaro, S.; Akhras, M.A.; Pucci, D.; Nori, F.
Towards Real-time Whole-Body Human Dynamics Estimation through Probabilistic Sensor Fusion Algorithms.
A Physical Human–Robot Interaction Case Study.
Autonomous Robots, Springer US, October 2018, doi:
10.1007/s10514-018-9808-4
https://doi.org/10.1007/s10514-018-9808-4
The bibtex code for including this citation is provided:
@Article{Latella2018,
author="Latella, Claudia
and Lorenzini, Marta
and Lazzaroni, Maria
and Romano, Francesco
and Traversaro, Silvio
and Akhras, M. Ali
and Pucci, Daniele
and Nori, Francesco",
title="Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms",
journal="Autonomous Robots",
year="2018",
month="Oct",
day="31",
issn="1573-7527",
doi="10.1007/s10514-018-9808-4",
url="https://doi.org/10.1007/s10514-018-9808-4"
}