A Threedimensional Finger Motion Measurement System of a Thumb and an Index Finger Without a Calibration Process Various wearable systems have been investigated to measure hand motion, but some challenges remain. Many systems require a calibration process to map sensor signals to actual finger joint angles by the principle of measuring the length change of the finger, or bending sensors. Also, few studies have investigated how to measure thumb motion accurately using the wearable systems. This paper proposes an exoskeleton system with linear Hall sensors to measure threedimensional hand motion without a calibration process. The calibration process is avoided by measuring finger joint angles through an absolute rotation measurement. A new wearing method with lower parts underneath the hand joints and rubber bands is proposed to fix the structure to the hand and adapt it for various hand sizes. As the thumb has a complex biomechanical feature at carpometacarpal (CMC) joint, a new measuring method of the CMC joint is proposed to directly calculate the orientation of the metacarpal. The prototype of the thumb and index finger was manufactured, and the performance was verified experimentally by using an optical motion capture system.

Palm Reading: Using Palm Deformation for Fingers and Thumb Pose Estimation Hand pose estimation is recognised as being one of the most challenging topics in the field of human pose estimation. Accurate estimation and tracking of multi degree of freedom hand joints can be beneficial to many research areas such as robotic telemanipulation, motion patterns, robotic hand design and, more generally, human computerrobot interaction. Current solutions to hand tracking are unsatisfactory due to numerous simplifications used in modeling of the hand kinematics and noiseprone hand and finger position sensing methods. In this paper, we propose alternative hand pose sensing approach that includes detecting palm shape in order to more accurately estimate joint angles of middle and index fingers and thumb. We use Inertia Measurement Unit (IMU) sensors on the palm to detect forming of palm arches in different fingers and thumbsxe2x80x99 poses. Principal component analysis as well as Dynamic Neural Networks are utilized to create three different models for fingers and thumb poses, while Polaris optical motion capture system is used as a ground truth. Validating through the unused data shows that using the palm shape as an additional parameter in hand tracking can estimate the hand digit joint angles with the average error of under 4.1%.