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

TL;DR

An Object Augmentation Algorithm capable of augmenting existing marker-based databases with virtual object motions and object-induced joint torque estimations is proposed and shown to be robust to variations in the number and placement of input markers, which are expected between databases.

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.

Figures (7)

• Figure 2: Top Panel: 4 DoF limb motion trajectories and motion pictograms. Bottom panel: Grasped experimental objects, representative virtual objects, with and marker placements.
• Figure 3: The 5-phase computation pipeline of the Object Augmentation Algorithm: 1. hand frame from input markers, 2. virtual object placement in hand frame, 3. virtual object trajectory, 4. object wrench from object motion and 5. joint torques from object wrench. Validation: Object grasping - object manipulation and tracking - torque measurements.
• Figure 4: Hand coordinate frame from hand $M_h$ and wrist $M_w$ markers.
• Figure 5: Sensitivity analysis outcome of OAA, where centroids of marker clusters 3, 4, 5 and 6 were calculated within a 6 cm diameter.
• Figure 6: (a) For case 1 -- point-wise L2 tracking errors between virtual ($VM$) and ground truth markers ($GM$). Mean errors are solid lines. Error bands are $\pm$ 1SD across three repetitions. Panels (b) and (c) show x, y, and z marker trajectories affixed onto each object. Colored lines are $VM$ case 1 trajectories. $GM$ trajectories are grey-scale dashed. Vertical, coloured bands indicate joint motions.