## 1 Introduction  
The hand is a complex interface that performs various manual tasks, such as manipulating objects, communicating, typing, and playing musical instruments. Digital human hand models have been widely used in ergonomic product design and evaluation [1; 2; 3; 4; 5; 6; 7]. For example, [PERSON] et al. [4] developed a system for the ergonomic design and assessment of a handheld information appliance by integrating a digital hand with a product model and corresponding tasks to save development time and cost. Most digital hand models in the market and existing studies have been established based on the assumption that the hand is a rigid linkage system, indicating that hand segments rotate around fixed joint centers of rotation (CoRs) in the models. Fixed joint CoRs can be estimated using surface marker-defined finger motions [8; 9; 10; 11] or bone curvature-based method [12; 13]. For example, [PERSON] et al. [11] estimated finger joint CoR locations from measured surface marker flexion-extension motions by minimizing the time-variance of the internal link lengths based on an empiricallyquantifiable relationship between the local movement of a surface marker around a joint and the joint flexion-extension angle. [PERSON] et al. (2013) reconstructed a 3D hand skeleton from magnetic resonance imaging (MRI) scans and estimated the location of a finger joint CoR as the center of curvature of the portion of the head of the proximal bone segment in contact with the base of the distal bone segment of the finger joint in the sagittal plane. Finite joint CoRs need to be estimated since the hand is actually a non-rigid linkage system. [PERSON] (2014) proposed a geometric method to estimate the locations of finite joint CoRs by measuring the relative displacement of two adjacent hand segments. [PERSON] (2015) validated [PERSON]'s method and found that the error in estimating the finite joint CoR decreased as the rotation angle of the joint increased. Based on [PERSON] report, to achieve relatively high accuracy (error \(<\) 2 mm) in finite joint CoR location estimation using [PERSON]'s method, the rotation angle of a joint needs to be larger than 20\({}^{\circ}\). [PERSON] et al. (2016) applied [PERSON]'s method to estimate the locations of finite finger joint CoRs by marking two points on the hand skin surface along the direction of the distal hand segment of a joint. [PERSON] et al. (2017) developed a method to estimate the locations of finite finger joint CoRs by aligning the proximal bone segments of a joint at different postures using the iterative closest point (ICP) algorithm ([PERSON] and [PERSON], 2018) to form the relative movement of the distal bone segments of the joint. However, as shown in Figure 1, adapted from [PERSON] et al. 's report, an obvious error among the estimated locations of finite finger joint CoRs appeared. The error could be caused by the differences in the 3D bone surfaces at different postures reconstructed from different computed tomography (CT) scans due to the intensity differences at different CT scans. The present study proposed to use the same bone surfaces at different postures for better estimation of the locations of finite finger joint CoRs. The 3D hand skeleton at the full extension posture was selected as the hand skeleton template. After the connected bone segments in the template were separated, each bone segment in the template was registered to the corresponding bone segment at a particular hand posture. Then, the registered hand skeletons at another hand posture with the template were used for estimation of the locations of finite finger joint CoRs. Through registration, the bone surfaces at particular postures were the same as those at the template posture. The proposed method was applied to find the locations of the finite finger joint CoRs at metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints of the index, middle, ring, and little fingers.