Low-Cost Portable System for Measurement and Representation of 3D Kinematic Parameters in Sport Monitoring: Discus Throwing as a Case Study.

Monitoring of sports practice has become an almost essential tool in high-level professional training. The knowledge of the exact movements performed by an athlete provides a great advantage over conventional training, since the best performance can be theoretically known in advance and the trainer will expect the real athlete's movements to approximate it. Following this trend, this article deals with the design and development of a low-cost wearable biofeedback system for the measurement and representation of kinematic parameters in 3D. To capture the athlete's movements, an inertial measurement unit (IMU) is used, whose data are processed in an microcontroller-based architecture. The kinematic parameters of the athlete's movement are sent via Bluetooth to a smart phone, where they are displayed graphically. Experimental examples show the effectiveness of the device developed and illustrate the key results derived.

Optimal sensor placement for multiple target positioning with range-only measurements in two-dimensional scenarios.

The problem of determining the optimal geometric configuration of a sensor network that will maximize the range-related information available for multiple target positioning is of key importance in a multitude of application scenarios. In this paper, a set of sensors that measures the distances between the targets and each of the receivers is considered, assuming that the range measurements are corrupted by white Gaussian noise, in order to search for the formation that maximizes the accuracy of the target estimates. Using tools from estimation theory and convex optimization, the problem is converted into that of maximizing, by proper choice of the sensor positions, a convex combination of the logarithms of the determinants of the Fisher Information Matrices corresponding to each of the targets in order to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. Analytical and numerical solutions are well defined and it is shown that the optimal configuration of the sensors depends explicitly on the constraints imposed on the sensor configuration, the target positions, and the probabilistic distributions that define the prior uncertainty in each of the target positions. Simulation examples illustrate the key results derived.

Sensor networks for optimal target localization with bearings-only measurements in constrained three-dimensional scenarios.

In this paper, we address the problem of determining the optimal geometric configuration of an acoustic sensor network that will maximize the angle-related information available for underwater target positioning. In the set-up adopted, a set of autonomous vehicles carries a network of acoustic units that measure the elevation and azimuth angles between a target and each of the receivers on board the vehicles. It is assumed that the angle measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Using tools from estimation theory, the problem is converted into that of minimizing, by proper choice of the sensor positions, the trace of the inverse of the Fisher Information Matrix (also called the Cramer-Rao Bound matrix) to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. It is shown that the optimal configuration of the sensors depends explicitly on the intensity of the measurement noise, the constraints imposed on the sensor configuration, the target depth and the probabilistic distribution that defines the prior uncertainty in the target position. Simulation examples illustrate the key results derived.