Low-cost real-time motion capturing system using inertial measurement units

Human movement modeling - also referred to as motion-capture - is a rapidly expanding field of interest for medical rehabilitation, sports training, and entertainment. Motion capture devices are used to provide a virtual 3-dimensional reconstruction of human physical activities - employing either optical or inertial sensors. Utilizing inertial measurement units and digital signal processing techniques offers a better alternative in terms of portability and immunity to visual perturbations when compared to conventional optical solutions. In this paper, a cable-free, low-cost motion-capture solution based on inertial measurement units with a novel approach for calibration is proposed. The goal of the proposed solution is to apply motion capture to the fields that, because of cost problems, did not take enough benefit of such technology (e.g., fitness training centers). According to this goal, the necessary requirement for the proposed system is to be low-cost. Therefore, all the considerations and all the solutions provided in this work have been done according to this main requirement

The Possibilistic Kalman Filter: Definition and Comparison with the Available Methods

The Kalman filter (KF) is a commonly used algorithm for predicting the state variables of a system. It is based on the model of the system and some measurements (observed over time) that are characterized by their own uncertainty. This article defines a possibilistic KF whose main feature is to predict the values of the state variables and the associated uncertainty when uncertainty contributions of nonrandom nature are present. This possibilistic KF is defined in the mathematical framework of the possibility theory and employs random-fuzzy variables and the related mathematics since these variables can properly represent measurement results together with the associated uncertainty. A comparison with the available methods is provided, as well as the final validation