Object Augmentation Algorithm: Computing virtual object motion and object induced interaction wrench from optical markers

TL;DR

This paper introduces an Object Augmentation Algorithm that estimates virtual object motion and induced joint torques from optical markers, enhancing datasets for human arm activity analysis involving objects.

Contribution

The study presents a novel algorithm capable of augmenting marker-based data with virtual object motions and joint torque estimations, filling a gap in human-object interaction data analysis.

Findings

Accurately estimates 6 DoF object motion with > 0.99 correlation.

Precisely computes object-induced joint torques with > 0.93 correlation.

Robust to marker placement variations, with minor differences in repeated tests.

Abstract

This study addresses the critical need for diverse and comprehensive data focused on human arm joint torques while performing activities of daily living (ADL). Previous studies have often overlooked the influence of objects on joint torques during ADL, resulting in limited datasets for analysis. To address this gap, we propose an Object Augmentation Algorithm (OAA) capable of augmenting existing marker-based databases with virtual object motions and object-induced joint torque estimations. The OAA consists of five phases: (1) computing hand coordinate systems from optical markers, (2) characterising object movements with virtual markers, (3) calculating object motions through inverse kinematics (IK), (4) determining the wrench necessary for prescribed object motion using inverse dynamics (ID), and (5) computing joint torques resulting from object manipulation. The algorithm's accuracy is validated through trajectory tracking and torque analysis on a 5+4 degree of freedom (DoF) robotic hand-arm system, manipulating three unique objects. The results show that the OAA can accurately and precisely estimate 6 DoF object motion and object-induced joint torques. Correlations between computed and measured quantities were > 0.99 for object trajectories and > 0.93 for joint torques. The OAA was further shown to be robust to variations in the number and placement of input markers, which are expected between databases. Differences between repeated experiments were minor but significant (p < 0.05). The algorithm expands the scope of available data and facilitates more comprehensive analyses of human-object interaction dynamics.