Gonzalo Olivares

HidroBus® system: fieldbus for integrated management of extensive areas of irrigated land

Abstract In this communication, we present the HidroBus system, which was specially designed for centralized remote control and supervision of large areas of irrigated land, with a large number of nodes, a distance of tens of kilometers from the nodes to the control center and no electricity supply to the remote stations that are to control the irrigation hydrants. We also describe, as an example of the practical application of this system, the automation of the irrigation at Jumilla (Murcia, Spain), featuring real-time control of 1850 hydrants on an individual basis and the optimization of irrigation to demand necessities, together with low installation and operating costs.

Wagyromag: Wireless sensor network for monitoring and processing human body movement in healthcare applications

Human body movement can be monitored through a wireless network composed of inertial sensors. This work presents the development of Wagyromag (Wireless Accelerometer, GYROscope and MAGnetometer), a wireless Inertial Measurement Unit (IMU) composed of a triaxial accelerometer, gyroscope and magnetometer. Communication is based on a 802.15.4 network. Furthermore, calibration, signal conditioning and signal processing algorithms are presented throughout this work. Wagyromags high potential permits its application in a wide range of medical applications such as telerehabilitation, nocturnal epilepsy seizure detection, fall detection and other applications in the field of sport science.

Detection of (In)activity Periods in Human Body Motion Using Inertial Sensors: A Comparative Study

Determination of (in)activity periods when monitoring human body motion is a mandatory preprocessing step in all human inertial navigation and position analysis applications. Distinction of (in)activity needs to be established in order to allow the system to recompute the calibration parameters of the inertial sensors as well as the Zero Velocity Updates (ZUPT) of inertial navigation. The periodical recomputation of these parameters allows the application to maintain a constant degree of precision. This work presents a comparative study among different well known inertial magnitude-based detectors and proposes a new approach by applying spectrum-based detectors and memory-based detectors. A robust statistical comparison is carried out by the use of an accelerometer and angular rate signal synthesizer that mimics the output of accelerometers and gyroscopes when subjects are performing basic activities of daily life. Theoretical results are verified by testing the algorithms over signals gathered using an Inertial Measurement Unit (IMU). Detection accuracy rates of up to 97% are achieved.

High-efficiency low-cost accelerometer-aided gyroscope calibration

Inertial measurement units (IMUs) are gaining popularity in several application fields, such as navigation, body motion monitoring and indoors positioning. Microelectromechanical (MEMS) accelerometers and gyroscopes are used for this purpose since they offer a reasonable price-performance trade-off. However, they still present several undesired characteristics in their output that should be compensated through proper device calibration. Although accelerometers are quite easy to calibrate, gyroscopes need more complex systems and equipment to achieve an accurate calibration. This paper shows a novel calibration system using simple equipment (a bike wheel as a turntable) designed for an IMU which is used for a knee telerehabilitation system and composed of a triaxial accelerometer and a biaxial gyroscope. MEMS Accelerometers are usually more accurate and offer better performance than MEMS gyroscopes. Thus, accelerometer data is used to help calibrate the gyroscope by applying a novel, simple, yet accurate set of maneuvers.

Automatic determination of validity of input data used in ellipsoid fitting MARG calibration algorithms.

Ellipsoid fitting algorithms are widely used to calibrate Magnetic Angular Rate and Gravity (MARG) sensors. These algorithms are based on the minimization of an error function that optimizes the parameters of a mathematical sensor model that is subsequently applied to calibrate the raw data. The convergence of this kind of algorithms to a correct solution is very sensitive to input data. Input calibration datasets must be properly distributed in space so data can be accurately fitted to the theoretical ellipsoid model. Gathering a well distributed set is not an easy task as it is difficult for the operator carrying out the maneuvers to keep a visual record of all the positions that have already been covered, as well as the remaining ones. It would be then desirable to have a system that gives feedback to the operator when the dataset is ready, or to enable the calibration process in auto-calibrated systems. In this work, we propose two different algorithms that analyze the goodness of the distributions by computing four different indicators. The first approach is based on a thresholding algorithm that uses only one indicator as its input and the second one is based on a Fuzzy Logic System (FLS) that estimates the calibration error for a given calibration set using a weighted combination of two indicators. Very accurate classification between valid and invalid datasets is achieved with average Area Under Curve (AUC) of up to 0.98.

Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters

With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. Therefore, in this work, we show that, by applying frequency analysis to determine motion intensity, and varying the formerly fixed parameters accordingly, the overall precision of orientation estimation algorithms can be improved, therefore providing physicians with reliable objective data they can use in their daily practice.

Parallel dynamic water supply scheduling in a cluster of computers

The parallelization of complex planning and control problems arising in diverse application areas in the industrial, services and commercial environments not only allows the determination of control variables in the required times but also improves the performance of the control procedure as more processors are involved in the execution of the parallel program. In this paper we describe a scheduling application in a water supply network to demonstrate the benefits of parallel processing. The procedure we propose combines dynamic programming with genetic algorithms and time series prediction in order to solve problems in which decisions are made in stages, and the states and control belong to a continuous space. Taking into account the computational complexity of these applications and the time constraints that are usually imposed, the procedure has been implemented by a parallel program in a cluster of computers, an inexpensive and widely extended platform that can make parallelism a practical means of tackling complex problems in many different environments. Copyright

Genetic algorithms and neuro-dynamic programming: application to water supply networks

Genetic algorithms, time series prediction, and Monte Carlo simulation are applied to dynamic programming in order to solve complex planning and control problems in which decisions are made in stages, and the states and control belong to a continuous space. Each decision has an immediate associated cost and also affects the cost of future stages. Therefore, a balance is required between a low cost solution at the present and the possible high costs in the future. A hybrid genetic algorithm is used to determine the feasible functioning states in each stage. A procedure for series prediction based on RBF networks allows the uncertainty about state transitions to be avoided and Monte Carlo simulations are used to approximate the cost-to-go function, thus reducing the computational cost of the dynamic programming procedure. As an example, the proposed procedure is applied to a water supply network scheduling problem.

Sensor fusion adaptive filtering for position monitoring in intense activities

Inertial sensors are widely used in body movement monitoring systems Different factors derived from the sensors nature, such as the Angle Random Walk (ARW), and dynamic bias lead to erroneous measurements Moreover, routines including intense exercises are subject to high dynamic accelerations that distort the angle measurement Such negative effects can be reduced through the use of adaptive filtering based on sensor fusion concepts Most existing published works use a Kalman filtering sensor fusion approach Our aim is to perform a comparative study among different adaptive filters Several Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters variations are tested with the purpose of finding the best method leading to a more accurate angle measurement An angle wander compensation and dynamic acceleration bursts filtering method has been developed by the implementing a sensor fusion approach based on LMS and RLS filters.

Accurate Human Limb Angle Measurement in Telerehabilitation: Sensor Fusion through Kalman, LMS and RLS Adaptive Filtering

Inertial sensors are widely used in telerehabilitation systems since they permit to monitor the patient’s movement and determine the position of their limbs. Limbs angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors nature, such as the Angle Random Walk (ARW), and dynamic bias lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several LMS and RLS variations are tested with the purpose of finding the best method leading to a more accurate limb angle measurement. An angle wander compensation sensor fusion approach based on Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters has been developed.