Carmen Sánchez Ávila

Bio: Carmen Sánchez Ávila is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Biometrics & Image segmentation. The author has an hindex of 10, co-authored 28 publications receiving 496 citations. Previous affiliations of Carmen Sánchez Ávila include Complutense University of Madrid & ETSI.

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

Abstract: A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.

A Survey of the Elliptic Curve Integrated Encryption Scheme

Abstract: This work has been partially supported by Ministerio de Ciencia e Innovacion (Spain) under the grant TEC2009-13964-C04-02 and Ministerio de Industria, Turismo y Comercio (Spain) in collaboration with CDTI and Telefonica I+D under the project Segur@ CENIT-2007 2004

Modeling and Detecting Aggressiveness From Driving Signals

Abstract: The development of advanced driver assistance systems (ADASs) will be a crucial element in the construction of future intelligent transportation systems with the objective of reducing the number of traffic accidents and their subsequent fatalities. Specifically, driving behaviors could be monitored online to determine the crash risk and provide warning information to the driver via their ADAS. In this paper, we focus on aggressiveness as one of the potential causes of traffic accidents. We demonstrate that aggressiveness can be detected by monitoring external driving signals such as lateral and longitudinal accelerations and speed. We model aggressiveness as a linear filter operating on these signals, thus scaling their probability distribution functions and modifying their mean value, standard deviation, and dynamic range. Next, we proceed to validate this model via an experiment, conducted under real driving conditions, involving ten different drivers, traveling a route with five different types of road sections, subject to both smooth and aggressive behaviors. The obtained results confirm the validity of the model of aggressiveness. In addition, they show the generality of this model and its applicability to specific driving signals (speed, longitudinal, and lateral accelerations), every single driver, and every road type. Finally, we build a classifier capable of detecting aggressive behavior from the driving signal. This classifier achieves a success rate up to 92%. Language: en

Stress detection by means of stress physiological template

Abstract: This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications.

Silhouette-based hand recognition on mobile devices

Abstract: Hand Biometric Recognition not only gathers a good performance in identifying individuals but also it is known to be a non-invasive biometric technique. Furthermore, there exist new trends towards mobile implementation developments, focusing on embedding current biometric systems in mobile devices. This paper aims to implement hand biometric recognition with mobile devices.

Inertial Sensor-Based Gait Recognition: A Review

Abstract: With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee

Abstract: Sometimes, one needs to control different emotional situations which can lead the person suffering them to dangerous situations, in both the medium and short term. There are studies which indicate that stress increases the risk of cardiac problems. In this study we have designed and built a stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee. In order to check the device's performance, we have used 16 adults (eight women and eight men) who completed different tests requiring a certain degree of effort, such as mathematical operations or breathing deeply. On completion, we appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%. In the future, we plan to create an algorithm which is able to differentiate between each state.