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32 results on '"Jimenez, Antonio R."'

7. Diversified local search for the optimal layout of beacons in an indoor positioning system

Author

Laguna, Manuel, Roa, Javier O., Jimenez, Antonio R., and Seco, Fernando

Subjects
Business, Engineering and manufacturing industries
Abstract

The navigation of Autonomous Guided Vehicles (AGVs) in industrial environments is often controlled by positioning systems based on landmarks or artificial beacons. In these systems, the position of an AGV navigating in an interior space is determined by the calculation of its relative distance to beacons, whose location is known in advance. A fundamental design problem associated with landmark navigation systems consists in determining the optimal location of the minimum number of beacons necessary to achieve a desired level of accuracy and reliability. A local search procedure coupled with a diversification strategy is developed for this problem. Comparisons with an earlier solution method based on genetic algorithms are provided and it is shown that the proposed procedure finds better designs in a fraction of the computational time employed by the genetic algorithm. Keywords: Optimal beacon placement, Diversified Local Search, Genetic Algorithm, Local Positioning System, 1. Introduction An Autonomous Guided Vehicle (AGV) must continuously determine its position within a manufacturing facility in order to find a path to a desired destination. While Global Positioning Systems [...]

Published
2009

8. Broadband EMFi-based transducers for ultrasonic air applications

Author

Ealo, Joao L., Seco, Fernando, and Jimenez, Antonio R.

Subjects
Broadband transmission -- Research, Electromechanical devices -- Usage, Ultrasonics -- Research, Broadband Internet, Business, Electronics, Electronics and electrical industries
Abstract

The usage of electromechanical film (EMFi) for developing broadband transducers for ultrasonic air applications is discussed. Wide usable range and easiness to use are found to be the main advantages of the EMFi.

Published
2008

31. Validation of an IMU-based Gait Analysis Method for Assessment of Fall Risk Against Traditional Methods.

Author

Garcia-de-Villa S, Ruiz LR, Neira GG, Alvarez MN, Huertas-Hoyas E, Del-Ama AJ, Rodriguez-Sanchez MC, Seco F, and Jimenez AR

Abstract

Falls are a severe problem in older adults, often resulting in severe consequences such as injuries or loss of consciousness. It is crucial to screen fall risk in order to prescribe appropriate therapies that can potentially prevent falls. Identifying individuals who have experienced falls in the past, commonly known as fallers, is used to evaluate fall risk, as a prior fall indicates a higher likelihood of future falls. The methods that have the most support from evidence are Gait Speed (GS) and Time Up and Go (TUG), which use specific cut-off values to evaluate the fall risk. There have been proposals for alternative methods that use wearable sensor technology to improve fall risk assessment. Although these technological alternatives are promising, further research is necessary to validate their use in clinical settings. In this study, we propose a method for identifying fallers based on a Support Vector Machine (SVM) classifier. The inputs for the classifier are the gait parameters obtained from a 30-minute walk recorded using an Inertial Measurement Unit (IMU) placed at the foot of patients. We validated our proposed method using a sample of 157 patients aged over 70 years. Our findings indicate significant differences (p< 0.05) in stride speed, clearance, angular velocity, acceleration, and coefficient of variability among steps between fallers and non-fallers. The proposed method demonstrates the its potential to classify fallers with an accuracy of [79.6]%, slightly outperforming the GS method which provides an accuracy of [77.0]%, and also overcomes its dependency on the cut-off speed to determine fallers. This method could be valuable in detecting fallers during long-term monitoring that does not require periodic evaluations in a clinical setting.

Published
2024
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