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 7+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.

翻译：本研究旨在解决关于人类手臂在执行日常生活活动（ADL）时关节扭矩的多样化与全面性数据的迫切需求。以往的研究常忽视ADL过程中物体对关节扭矩的影响，导致可用于分析的数据集有限。为填补这一空白，我们提出了一种物体增强算法（OAA），能够利用虚拟物体运动与物体诱导的关节扭矩估计来增强现有的基于标记的数据库。OAA包含五个阶段：（1）基于光学标记计算手部坐标系，（2）利用虚拟标记表征物体运动，（3）通过逆运动学（IK）计算物体运动，（4）使用逆动力学（ID）确定实现指定物体运动所需的力矩，（5）计算由物体操控产生的关节扭矩。该算法的准确性通过在一个具有7+4自由度（DoF）的机器人手-臂系统上操控三种不同物体进行轨迹跟踪与扭矩分析得到验证。结果表明，OAA能够准确且精确地估计6自由度的物体运动及物体诱导的关节扭矩。计算量与测量量之间的相关性在物体轨迹上大于0.99，在关节扭矩上大于0.93。进一步证明，OAA对输入标记的数量和位置变化具有鲁棒性，这种变化在数据库之间是预期存在的。重复实验之间的差异虽小但显著（p < 0.05）。该算法拓展了可用数据的范围，并促进了对人-物交互动力学更全面的分析。