Assistive and rehabilitation robotics have gained momentum over the past decade and are
expected to progress significantly in the coming years. Although relevant and promising research advances
have contributed to these fields, challenges regarding intentional physical contact with humans remain.
Despite being a fundamental component of assistive and rehabilitation tasks, there is an evident lack of
work related to robotic manipulators that intentionally manipulate human body parts. Moreover, existing
solutions involving end-effector robots are not based on accurate knowledge of human limb dimensions and
their current configuration. This knowledge, which is essential for safe human–limb manipulation, depends
on the grasping location and human kinematic parameters. This paper addresses the upper-limb manipulation
challenge and proposes a pose estimation method using a compliant robotic manipulator. To the best of our
knowledge, this is the first attempt to address this challenge. A kinesthetic-based approach enables estimation
of the kinematic parameters of the human arm without integrating external sensors. The estimation method
relies only on proprioceptive data obtained from a collaborative robot with a Cartesian impedance-based
controller to follow a compliant trajectory that depends on human arm kinodynamics. The human arm
model is a 2-degree of freedom (DoF) kinematic chain. Thus, prior knowledge of the arm’s behavior and an
estimation method enables estimation of the kinematic parameters. Two estimation methods are implemented
and compared: i) Hough transform (HT); ii) least squares (LS). Furthermore, a resizable, sensorized dummy
arm is designed for experimental validation of the proposed approach. Outcomes from six experiments with
different arm lengths demonstrate the repeatability and effectiveness of the proposed methodology, which
can be used in several rehabilitation robotic applications.
