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 car ried 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 innova tive 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 χ2 distribution. Entire process is executed on the smartphone, by means of taking the system energy consumption into ac count, thereby increasing the battery lifetime and minimizing the device recharging frequency.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Hi-Res activity recognition system based on EEG and WoT

Nowadays, the recognition of physical activity (PA) is a well-known problem with many solutions. Sev eral kind of algorithms, using MEMS sensors, al low determine the most likely activity. Indeed, these applications work well when physical activity is performed for long periods of time and steadily. However, indoors, these systems are not entirely suitable and have several problems. In this paper, thanks to the introduction of new context infor mation, such as EEG, and through communication between WoT based elements interface at home, it would be possible to perform a more accurate and low-level recognition. By using uPnP proto col and additional services, information from other smart housing elements with user device itself can be shared, enriching traditional systems based on ac-celerometry.Ministerio de Economía y Competitividad TIN2009-14378-C02-01Junta de Andalucía TIC-805

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

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.Junta de Andalucia Simon TIC-805

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 2 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.Ministerio de Economía y Competitividad TIN2013-46801-C4-1-rJunta de Andalucía TIC-805

Discrete classification technique applied to TV advertisements liking recognition system based on low‑cost EEG headsets

Background: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. Methods: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. Results: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. Conclusions: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.Ministerio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucia Simon TIC-805

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

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

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.This research was funded by the Spanish Ministry of Education, Culture and Sports under grant number FPU13/03917