## 2 Background and Related Literature  
Researchers have been investigating and developing different kinds of hand\(-\)tracking systems [4; 6; 8; 10; 16; 17; 18; 19; 20; 21; 22; 23] that can find applications in various fields such as space exploration, surgical robotics, search and rescue operations, manufacturing sector, etc. These systems can be classified as either contactless or wearable. The contactless tracking systems are usually vision\(-\)based with recent versions using non\(-\)contact inductive sensors to track hand gestures [23]. Vision\(-\)based systems typically use multiple cameras (RGB depth or infrared sensors) or a combination of them [6; 16]. [PERSON] et al. [18] developed a vision\(-\)based system to estimate the motion of human finger and thumb joints by tracking the reflective markers placed on the joints. [PERSON] et al. [4] developed a markerless vision\(-\)based hand\(-\)tracking system to telemanipulate a robot hand using four RGB\(-\)D cameras. They used point cloud data to train a neural network to estimate the hand pose. Commercially available vision\(-\)based systems that track markers accurately are generally expensive; a basic vision\(-\)based motion capture system with software such as VICON cost approximately 12,500 USD in 2023 [24]. However, vision\(-\)based systems experience issues such as motion blur, occlusions, and limited workspace. Systems with multiple cameras need to be employed at known fixed locations, making these system not easily portable and increasing the system cost. An alternative approach has been to use other sensors such as magnetic\(-\)based, optical linear encoders, potentiometers, IMUs (Inertial Measurement Units), and flex and stretch sensors to develop wearable hand\(-\)tracking systems [8; 19; 20; 21; 22; 25; 26]. [PERSON] et al. [19] used linear potentiometers to capture the motion of the human hand, but they were able to capture only the FE motion of the CMC joint. [PERSON] et al. [22] developed a glove using magnetic and IMU sensors and used quaternion-based extended Kalman filters to estimate the kinematic model parameters for hand\(-\)arm tracking. IMUs and magnetic sensors generally require chronic recalibration due to the accumulation of errors from sensor drift over time. Researchers also investigated the use of soft or stretch sensors in wearable hand\(-\)tracking systems due to advancements in soft sensor technology and their ability to provide unrestricted finger motion [27; 28; 29; 30; 31]. [PERSON] et al. [8] developed a glove outfitted with 44 stretchable capacitive silicone sensors to capture finger motion using a deep network architecture to reconstruct sensed skin deformations. Under cyclic stretching/releasing, the stretch sensors experience difficulty in recovery and suffer from hysteresis, signal drift, and sensitivity to environmental influence [6; 32]. Flex or -bend sensors have been successful in reliably tracking the bending of one\(-\)DOF hand joints [25; 26] and adopted by commercial gloves such as the CyberGlove(r)[33], the Manus VR glove [34], and the VRFree glove [35]. The latter two gloves also use IMU sensors in addition to flex sensors to track hand motion. In general, hand\(-\)tracking systems are widely employed for the telemanipulation of ARHs. However, CMC joint tracking is still challenging and requires expensive hardware or not easily personalizable setups. Regardless of the tracking systems used, the captured human hand motion cannot be directly mapped to a robot thumb due to their kinematic differences such as joint configuration, DOFs, dimensions, workspace, and user\(-\)specific calibration [9]. The most commonly used mapping approaches are joint and Cartesian space. Joint space mapping is simple, since it does not provide direct control over the finger/thumb tip location and might result in different poses depending on kinematic parameters and configuration differences. Joint space mapping is mostly suited for power grasps.