Miguel Ángel Álvarez de la Concepción

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.

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.

Evaluating Wearable Activity Recognition and Fall Detection Systems

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.

Activity Recognition System Using Non-intrusive Devices through a Complementary Technique Based on Discrete Methods

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.

Activity Recognition System Using AMEVA Method

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.

An adaptive methodology to discretize and select features

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.