This paper presents a new algorithm for the real-time representation of a spine using a mobile device for recording and analyzing the human body posture. The measurements are based on inertial measuring units (IMU), which are providing the pitch angles of five points of a cord attached to the clothing (t-shirt or jacket). The algorithm developed for the real-time representation of the spine is based on quadratic Bézier curves defined in a reference frame attached to the first sensor from the head. The curve is obtained knowing the distances between the sensors and the measured pitch angles. The line of the spine is reconstructed with four segments of Bézier curves, meeting the requirement of continuity of the tangents. The obtained postures are compared with a normal one, identified by the medical specialist, by computing the average absolute distances between them. The recorded movement of the spine can be used for diagnosis purposes, and the real-time tracking for therapy by warning the user to correct his/her posture. The warning message is delivered with buzzers attached to the clothing by applying haptic stimuli on the back.

The aim of this paper is to identify the number of parameters of a hand model which can be computed during virtual grasping. The fingers of the user are tracked with various methods (position sensors and bending sensors) and the measured data is used to estimate the unknown parameter of the hand. An articulated hand model is constructed based on the assumption that the measured data is corrupted by noise and some of the hand's physical parameters are known. Virtual grasping and the parametric hand model are important, especially for force rendering in haptic feedback computation and to produce visually realistic results in hand-artifact interaction.