2022
Khoramshahi, Mahdi; Roby-Brami, Agnes; Parry, Ross; Jarrassé, Nathanaël
In: PLOS ONE, vol. 17, no. 12, pp. e0278228, 2022, ISSN: 1932-6203, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: Body weight, Hip, Kinematics, Prosthetics, Robotics, Shoulders, Skeletal joints, Velocity
@article{khoramshahi_identification_2022b,
title = {Identification of inverse kinematic parameters in redundant systems: Towards quantification of inter-joint coordination in the human upper extremity},
author = { Mahdi Khoramshahi and Agnes Roby-Brami and Ross Parry and Nathanaël Jarrassé},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278228},
doi = {10.1371/journal.pone.0278228},
issn = {1932-6203},
year = {2022},
date = {2022-01-01},
urldate = {2024-02-12},
journal = {PLOS ONE},
volume = {17},
number = {12},
pages = {e0278228},
abstract = {Understanding and quantifying inter-joint coordination is valuable in several domains such as neurorehabilitation, robot-assisted therapy, robotic prosthetic arms, and control of supernumerary arms. Inter-joint coordination is often understood as a consistent spatiotemporal relation among kinematically redundant joints performing functional and goal-oriented movements. However, most approaches in the literature to investigate inter-joint coordination are limited to analysis of the end-point trajectory or correlation analysis of the joint rotations without considering the underlying task; e.g., creating a desirable hand movement toward a goal as in reaching motions. This work goes beyond this limitation by taking a model-based approach to quantifying inter-joint coordination. More specifically, we use the weighted pseudo-inverse of the Jacobian matrix and its associated null-space to explain the human kinematics in reaching tasks. We propose a novel algorithm to estimate such Inverse Kinematics weights from observed kinematic data. These estimated weights serve as a quantification for spatial inter-joint coordination; i.e., how costly a redundant joint is in its contribution to creating an end-effector velocity. We apply our estimation algorithm to datasets obtained from two different experiments. In the first experiment, the estimated Inverse Kinematics weights pinpoint how individuals change their Inverse Kinematics strategy when exposed to the viscous field wearing an exoskeleton. The second experiment shows how the resulting Inverse Kinematics weights can quantify a robotic prosthetic arm’s contribution (or the level of assistance).},
note = {Publisher: Public Library of Science},
keywords = {Body weight, Hip, Kinematics, Prosthetics, Robotics, Shoulders, Skeletal joints, Velocity},
pubstate = {published},
tppubtype = {article}
}
Understanding and quantifying inter-joint coordination is valuable in several domains such as neurorehabilitation, robot-assisted therapy, robotic prosthetic arms, and control of supernumerary arms. Inter-joint coordination is often understood as a consistent spatiotemporal relation among kinematically redundant joints performing functional and goal-oriented movements. However, most approaches in the literature to investigate inter-joint coordination are limited to analysis of the end-point trajectory or correlation analysis of the joint rotations without considering the underlying task; e.g., creating a desirable hand movement toward a goal as in reaching motions. This work goes beyond this limitation by taking a model-based approach to quantifying inter-joint coordination. More specifically, we use the weighted pseudo-inverse of the Jacobian matrix and its associated null-space to explain the human kinematics in reaching tasks. We propose a novel algorithm to estimate such Inverse Kinematics weights from observed kinematic data. These estimated weights serve as a quantification for spatial inter-joint coordination; i.e., how costly a redundant joint is in its contribution to creating an end-effector velocity. We apply our estimation algorithm to datasets obtained from two different experiments. In the first experiment, the estimated Inverse Kinematics weights pinpoint how individuals change their Inverse Kinematics strategy when exposed to the viscous field wearing an exoskeleton. The second experiment shows how the resulting Inverse Kinematics weights can quantify a robotic prosthetic arm’s contribution (or the level of assistance).
2021
Khoramshahi, Mahdi; Morel, Guillaume; Jarrassé, Nathanael
Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots Proceedings Article
In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 10453–10460, 2021, (ISSN: 2577-087X).
Abstract | Links | BibTeX | Tags: Collaboration, Costs, Ergonomics, Estimation, Kinematics, Redundancy, Wearable robots
@inproceedings{khoramshahi_intent-aware_2021-1,
title = {Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots},
author = { Mahdi Khoramshahi and Guillaume Morel and Nathanael Jarrassé},
doi = {10.1109/ICRA48506.2021.9561351},
year = {2021},
date = {2021-05-01},
booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages = {10453–10460},
abstract = {Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.},
note = {ISSN: 2577-087X},
keywords = {Collaboration, Costs, Ergonomics, Estimation, Kinematics, Redundancy, Wearable robots},
pubstate = {published},
tppubtype = {inproceedings}
}
Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and cost-minimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.