Miguel Ángel Álvarez de la Concepción
University of Seville
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Sensors | 2015
Luis Miguel Soria Morillo; Luis Gonzalez-Abril; Juan Antonio Ortega Ramírez; Miguel Ángel Álvarez de la Concepción
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.
Pervasive and Mobile Computing | 2017
Miguel Ángel Álvarez de la Concepción; Luis Miguel Soria Morillo; Juan Antonio Alvarez García; Luis Gonzalez-Abril
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.
Archive | 2015
Juan Antonio Álvarez-García; Luis Miguel Soria Morillo; Miguel Ángel Álvarez de la Concepción; Alejandro Fernández-Montes; Juan Antonio Ortega Ramírez
Activity recognition (AR) and fall detection (FD) research areas are very related in assistance scenarios but evolve independently. Evaluate them is not trivial and the lack of FD real-world datasets implies a big issue. A protocol that fuses AR and FD is proposed to achieve a large, open and growing dataset that could, potentially, provide an enhanced understanding of the activities and fall process and the information needed to design and evaluate high-performance systems.
International Competition on Evaluating AAL Systems through Competitive Benchmarking | 2013
Miguel Ángel Álvarez de la Concepción; Luis Miguel Soria Morillo; Luis González Abril; Juan Antonio Ortega Ramírez
This paper aims to develop a cheap, comfortable and, specially, efficient system which controls the physical activity carried out by the user. For this purpose an extended approach to physical activity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innovative selection, discretization and classification technique to make the recognition process in an efficient way and at low energy cost, is presented in this work based on Ameva discretization. Entire process is executed on the smartphone and on a wireless health monitoring system is used when the smartphone is not used taking into account the system energy consumption.
International Competition on Evaluating AAL Systems through Competitive Benchmarking | 2012
Luis Miguel Soria Morillo; Luis Gonzalez-Abril; Miguel Ángel Álvarez de la Concepción; Juan Antonio Ortega Ramírez
This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity carried out by the user. For this purpose an innovative approach to physical activity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innovative discretization and classification technique to make the recognition process in an efficient way and at low energy cost, is presented in this work based on the χ 2 distribution. Entire process is executed on the smartphone, by means of taking the system energy consumption into account, thereby increasing the battery lifetime and minimizing the device recharging frequency.
Artificial Intelligence Review | 2013
Miguel Ángel Álvarez de la Concepción; Luis González Abril; Luis Miguel Soria Morillo; Juan Antonio Ortega Ramírez
A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined.
ambient intelligence | 2013
Juan Antonio Ortega Ramírez; Luis Miguel Soria Morillo; Miguel Ángel Álvarez de la Concepción; Rafael Vergara
ambient intelligence | 2013
Juan Antonio Ortega Ramírez; Luis Miguel Soria Morillo; Miguel Ángel Álvarez de la Concepción; V. Montes; J.M. Chincho
XII Jornadas de Arca: Eficiencia Energética y Sostenibilidad en Inteligencia Artificial, 2011, ISBN 9788461464579, págs. 83-87 | 2011
Juan Antonio Ortega Ramírez; Luis Miguel Soria Morillo; Miguel Ángel Álvarez de la Concepción; Jesús Torres-Valderrama; Antonio Manuel Bellido Romero
XII Jornadas de Arca: Eficiencia Energética y Sostenibilidad en Inteligencia Artificial, 2011, ISBN 9788461464579, págs. 101-106 | 2011
Juan Antonio Ortega Ramírez; Francisco Javier Cuberos García-Baquero; Luis González Abril; Miguel Ángel Álvarez de la Concepción