Estimation of Forearm Pose Based on Upper Arm Deformation Using a Deep Neural Network Activities of body tissues such as muscles, tendons, and bones appear in the deformation of skins. It would be possible to obtain the information of the body motions by measuring the skin deformation. In our research, we focused on upper arm deformation, which is changed with not only the elbow flexion and extension but also the forearm pronation and supination. Thus, with the measurement of the upper arm deformation, we can estimate the elbow pose. In this paper, we propose a forearm pose estimation method based on upper arm deformation. A distance sensor array with eight sensor units is developed to measure the upper arm deformation. From the measured deformation, a deep neural network (DNN) based model is trained and used to estimate the forearm pose. The results of experiment show that our proposed method was able to estimate the forearm pose with RMSE of 2.9 degrees for the elbow joint angle and 7.6 degrees for the forearm pronation and supination angle.

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%.