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

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 requiredMinisterio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucía Simon P11-TIC-8052Junta de Andalucía M-Learning P11-TIC-712

Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970s made Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks

Desarrollo y versatilidad del algoritmo de discretización Ameva.

Esta tesis presentada como un compendio de artículos, analiza el problema de reconocimiento de actividades y detección de caídas en dispositivos móviles donde el consumo de batería y la precisión del sistema son las principales áreas de investigación. Estos problemas se abordan mediante el establecimiento de un nuevo algoritmo de selección, discretización y clasificación basado en el núcleo del algoritmo Ameva. Gracias al proceso de discretización, se obtiene un sistema eficiente en términos de energía y precisión. El nuevo algoritmo de reconocimiento de actividad ha sido diseñado para ejecutarse en dispositivos móviles y smartphones, donde el consumo de energía es la característica más importante a tener en cuenta. Además, el algoritmo es eficiente en términos de precisión dando un resultado en tiempo real. Estas características se probaron tanto en una amplia gama de dispositivos móviles utilizando diferentes datasets del estado del arte en reconocimiento de actividades así como en escenarios reales como la competición EvAAL donde personas no relacionadas con el equipo de investigación llevaron un smartphone con el sistema desarrollado. En general, ha sido posible lograr un equilibrio entre la precisión y el consumo de energía. El algoritmo desarrollado se presentó en el track de reconocimiento de actividades de la competición EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), que tiene como objetivo principal la medición del rendimiento de hardware y software. El sistema fue capaz de detectar las actividades a través del conjunto establecido de puntos de referencia y métricas de evaluación. Se desarrolló para varias clases de actividades y obtiene una gran precisión cuando hay aproximadamente el dataset está balanceado en cuanto al número de ejemplos para cada clase durante la fase de entrenamiento. La solución logró el primer premio en la edición de 2012 y el tercer premio en la edición de 2013.This thesis, presented as a set of research papers, studies the problem of activity recognition and fall detection in mobile systems where the battery draining and the accuracy are the main areas of researching. These problems are tackled through the establishment of a new selection, discretization and classification algorithm based on the core of the algorithm Ameva. Thanks to the discretization process, it allows to get an efficient system in terms of energy and accuracy. The new activity recognition algorithm has been designed to be run in mobile systems, smartphones, where the energy consumption is the most important feature to take into account. Also, the algorithm had to be efficient in terms of accuracy giving an output in real time. These features were tested both in a wide range of mobile devices by applying usage data from recognized databases and in some real scenarios like the EvAAL competition where non-related people carried a smartphone with the developed system. In general, it had therefore been possible to achieve a trade-off between accuracy and energy consumption. The developed algorithm was presented in the Activity Recognition track of the competition EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), which has as main objective the measurement of hardware and software performance. The system was capable of detecting some activities through the established set of benchmarks and evaluation metrics. It has been developed for multi-class datasets and obtains a good accuracy when there is approximately the same number of examples for each class during the training phase. The solution achieved the first award in 2012 competition and the third award in 2013 edition