Luis Gonzalez-Abril

Ameva: An autonomous discretization algorithm

This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time.

Online motion recognition using an accelerometer in a mobile device

Highlights? A statistical study and machine learning algorithms are used to interpret data. ? Different human activities can be effectively recognized. ? An approach to recognize motions either offline or online based on data in a mobile device. This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.

A Note on the Bias in SVMs for Multiclassification

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.

Low Energy Physical Activity Recognition System on Smartphones

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.

A study on saving energy in artificial lighting by making smart use of wireless sensor networks and actuators

This article is focused on adapting lighting conditions to user lighting preferences. A theoretical analysis of lighting conditions is carried out, and a case study is shown by means of the setup of an experimental environment and an empirical analysis of lighting conditions. Finally, a methodology for saving energy, which adjusts luminance to user preferences, is presented, and a study of the consumption results is given.

GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither modifies the original optimization problem for SVM training, nor introduces new hyper-parameters. Experimentation carried out on a high number of databases (23) shows GSVM obtaining the desired balanced accuracy between classes. Furthermore, its performance improves well-known cost-sensitive schemes for SVM, without adding complexity or computational cost.

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Abstract Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.

Support vector machines for interval discriminant analysis

The use of data represented by intervals can be caused by imprecision in the input information, incompleteness in patterns, discretization procedures, prior knowledge insertion or speed-up learning. All the existing support vector machine (SVM) approaches working on interval data use local kernels based on a certain distance between intervals, either by combining the interval distance with a kernel or by explicitly defining an interval kernel. This article introduces a new procedure for the linearly separable case, derived from convex optimization theory, inserting information directly into the standard SVM in the form of intervals, without taking any particular distance into consideration.

A model for the qualitative description of images based on visual and spatial features

An approach that provides a qualitative description of any image is presented in this paper. The main visual features (shape and colour) and the main spatial features (fixed orientation, relative orientation and topology) of each object within the image are described. This approach has been tested in two real scenarios that involve agents and human interaction: (i) images captured by the webcam of a mobile robot while it navigates, and (ii) images of tile compositions captured by an industrial camera used to select tile pieces to be used in assembling tile mosaics. In both scenarios, promising results have been obtained.

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Abstract Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.