Competitive Live Evaluation of Activity-recognition Systems

In order to ensure the validity and usability of activity recognition approaches, an agreement on a set of standard evaluation methods is needed. Due to the diversity of the sensors and other hardware employed, designing and accepting standard tests is a difficult task. This article presents an initiative to evaluate activity recognition systems: a living-lab evaluation established through an annual competition − EvAAL-AR (Evaluating Ambient Assisted Living Systems through Competitive Benchmarking − Activity Recognition). In the competition, each team brings their own activity-recognition system, which is evaluated live on the same activity scenario performed by an actor. The evaluation criteria attempt to capture the practical usability: recognition accuracy, user acceptance, recognition delay, installation complexity, and interoperability with ambient assisted living systems. The article also presents the competing systems with emphasis on two best-performing ones: (i) a system that achieved the best recognition accuracy, and (ii) a system that was evaluated as the best overall. Finally, the article presents lessons learned from the competition and ideas for future development of the competition and of the activity recognition field in general

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

Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

Deep Learning techniques for recognizing body movements

Trabajo de Fin de Grado en Ingeniería del Software, Facultad de Informática UCM, Departamento de Ingeniería de Software e Inteligencia Artificial, Curso 2019/2020En este trabajo se detalla el diseño y desarrollo de un sistema de reconocimiento de actividades humanas (HAR, human activity recognition) mediante redes neuronales, así como la labor de investigación realizada sobre técnicas y proyectos similares realizados sobre este mismo campo. El sistema desarrollado consta de varios módulos que hacen uso e interactúan a través de internet, estando en parte alojados en la nube. Concretamente, el diseño de la aplicación contiene dos módulos principales, una aplicación móvil para sistemas Android y una aplicación web alojada en un servidor. La primera, instalada en un dispositivo móvil, recaba datos de los sensores del dispositivo (sensores de aceleración y movimiento) y los envía al servidor por internet para ser analizados. La aplicación web consiste en una api REST, que hace uso de una red neuronal entrenada para realizar predicciones sobre la actividad que se está realizando, basándose en los datos recibidos. El tipo de red empleado es una red recurrente de tipo Long-Short-Term Memory (LSTM), basada en los trabajos publicados por Matlab sobre clasificación y predicción sequence-to-sequence. Esta herramienta es también la que se ha empleado para desarrollar el modelo de red y realizar los entrenamientos necesarios. El proceso de predicción se inicia cuando el usuario selecciona la opción correspondiente en su dispositivo móvil. Desde ese momento, cada dos centésimas de segundo se envía por internet un paquete de datos al servidor, conteniendo información sobre la situación actual en la que se encuentra el aparato según la actividad que se esté realizando. El servidor analiza cada uno de estos paquetes, los procesa y se los pasa a la red neuronal. Ésta realiza una predicción, con cierto grado de seguridad, que se almacena en la base de datos del servidor. Además de realizar predicciones, la aplicación móvil permite también enviar datos con una clase asociada relativa a la actividad que se está realizando. Estos datos se almacenan también en la base de datos y se pueden utilizar posteriormente para reentrenar la red y realizar nuevas pruebas. Al acceder a la aplicación web a través de una URL desde un navegador, se puede abrir una página web donde se muestra, en tiempo real, la actividad que la red predice que está realizando actualmente el usuario que lleva el dispositivo móvil, junto con el porcentaje de acierto respecto de la predicción realizada. Se describen las fases de desarrollo de la aplicación comenzando por el estudio y análisis de algoritmos de deep-learning a este ámbito de reconocimiento de actividades humanas, así como todo el proceso de ensayo y pruebas realizado sobre distintos modelos de redes para obtener los resultados más óptimos, con resultados satisfactorios.This work explains the design and development of a human activity recognition system (HAR) using neural networks, as well as the research work carried out over this specific field during the process. The developed system consists of several modules that use and interact through the internet, being partly hosted in the cloud. Specifically, the design of the application consists of two main modules, a smartphone application developed for Android systems and a web application deployed on a server. The Smartphone app collects data from the sensors on the mobile device (acceleration and movement sensors), sending them to the server through the internet to be analysed. The web application consists of a REST api, that uses a previously trained neural network to make predictions about the activity that its being performed based on the received data. The type of network used is a recurrent network of type long-short-term memory (LSTM), based on the works published by Matlab about sequence-to-sequence classification and prediction. This is also the tool used to develop the network model and to perform the required training processes. The prediction process starts when the user selects the corresponding option on his smartphone. From that moment, every two hundredths of a second a data packag e is sent through the internet to the server, containing information about the current situation of the device, depending on the activity being carried out. The server analyses every one of this packages, processes them, and feeds them to the neural networ k. It then makes a prediction, with a certain degree of reliability, which is stored on the server database. Besides the prediction making, the app also allows for sending data tagged with the corresponding class describing the activity currently being performed. This data is also stored in the database and can be used later to train and test the net work. When accessing the web application through a URL in a browser a webpage can be expanded displaying, in real time, the current activity predicted by the network, which is carried out by the user bearing the smartphone, is shown, alongside the certainty of the prediction made. The development phases of the application are described, starting with the study and analysis of deep-learning algorithms in this field of HAR, as well as the entire trial and testing process carried out on different network models to obtain the optimal solutions, with satisfactory results.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Time management with the help of modern information technology

Nowadays we aim to perform a high number of tasks on a daily basis, therefore the increase in the need for time management. Our wish and determination to change our habits do not suffice. Successful changes of our habits require not only planning but foremost the analysis of our current way of working and behaving. We need to be aware of the limited number of hours available which is why it is important how we take advantage of them. Project time management is a process of proper time planning spent on an individual task. The first step to time management is the analysis of the time spent. Problems, also known as time wasters, often occur during analysis. Because they are so similar, it is important to help ourselves by writing a journal. Following the analysis, is the plan of measures which define our intended changes. We determine which time waster we start with. The next step is writing a list of tasks needed or wished to be performed. By doing so, we clear our minds that is why, we are able to think more efficiently and more productively. Extensive tasks are divided into smaller more manageable parts. We set our aims, deadlines and priorities. When performing, we normally start with the most important tasks. This makes it possible to always finish them on time. There has been an expansion of time management methods in the last two decades. Two of them stand out, namely GTD Method (Getting Things Done) and Pomodoro Technique. GTD Method consists of five steps: collecting, processing, organising, reviewing and doing. It deals with task organisation which means that we are familiar with when and which task is being performed without being overloaded with other tasks. Since it does not say much about performing individual tasks, there is another method which does, namely Pomodoro Technique. It consists of five steps: planning, tracking, recording, processing and visualising. To manage time efficiently, we can help ourselves by using mobile devices, especially social mobile devices, including smartphones, tablets and smartwatches. Having quite a few advantages and unfortunately also disadvantages, social mobile devices influence other areas, such as business, education, health sectors, human psychology and social life. To manage time efficiently using social mobile devices, we need to have a proper application installed. Existing applications are not only meant to analyse personal performance but also to follow project time at work which is issued with invoices. To do analysis, we need qualitative reports. That is the purpose of the two applications developed within this Master’s thesis. They are basic applications recording our daily activities. They have a good visual display of the recorded data. \sn{Time Usage App} is an internet application which can be run at any display device. It requires internet access. \sn{Izraba časa} is the second application meant for devices with Windows 10 operating system. It is designed for mobile phones but can be used on other devices as well

Mobile-based online data mining : outdoor activity recognition

One of the unique features of mobile applications is the context awareness. The mobility and power afforded by smartphones allow users to interact more directly and constantly with the external world more than ever before. The emerging capabilities of smartphones are fueling a rise in the use of mobile phones as input devices for a great range of application fields; one of these fields is the activity recognition. In pervasive computing, activity recognition has a significant weight because it can be applied to many real-life, human-centric problems. This important role allows providing services to various application domains ranging from real-time traffic monitoring to fitness monitoring, social networking, marketing and healthcare. However, one of the major problems that can shatter any mobile-based activity recognition model is the limited battery life. It represents a big hurdle for the quality and the continuity of the service. Indeed, excessive power consumption may become a major obstacle to broader acceptance context-aware mobile applications, no matter how useful the proposed service may be. We present during this thesis a novel unsupervised battery-aware approach to online recognize users’ outdoor activities without depleting the mobile resources. We succeed in associating the places visited by individuals during their movements to meaningful human activities. Our approach includes novel models that incrementally cluster users’ movements into different types of activities without any massive use of historical records. To optimize battery consumption, our approach behaves variably according to users’ behaviors and the remaining battery level. Moreover, we propose to learn users’ habits in order to reduce the activity recognition computation. Our innovative battery-friendly method combines activity recognition and prediction in order to recognize users’ activities accurately without draining the battery of their phones. We show that our approach reduces significantly the battery consumption while keeping the same high accuracy. Une des caractéristiques uniques des applications mobiles est la sensibilité au contexte. La mobilité et la puissance de calcul offertes par les smartphones permettent aux utilisateurs d’interagir plus directement et en permanence avec le monde extérieur. Ces capacités émergentes ont pu alimenter plusieurs champs d’applications comme le domaine de la reconnaissance d’activités. Dans le domaine de l'informatique omniprésente, la reconnaissance des activités humaines reçoit une attention particulière grâce à son implication profonde dans plusieurs problématiques de vie quotidienne. Ainsi, ce domaine est devenu une pièce majeure qui fournit des services à un large éventail de domaines comme la surveillance du trafic en temps réel, les réseaux sociaux, le marketing et la santé. Cependant, l'un des principaux problèmes qui peuvent compromettre un modèle de reconnaissance d’activité sur les smartphones est la durée de vie limitée de la batterie. Ce handicap représente un grand obstacle pour la qualité et la continuité du service. En effet, la consommation d'énergie excessive peut devenir un obstacle majeur aux applications sensibles au contexte, peu importe à quel point ce service est utile. Nous présentons dans de cette thèse une nouvelle approche non supervisée qui permet la détection incrémentale des activités externes sans épuiser les ressources du téléphone. Nous parvenons à associer efficacement les lieux visités par des individus lors de leurs déplacements à des activités humaines significatives. Notre approche comprend de nouveaux modèles de classification en ligne des activités humaines sans une utilisation massive des données historiques. Pour optimiser la consommation de la batterie, notre approche se comporte de façon variable selon les comportements des utilisateurs et le niveau de la batterie restant. De plus, nous proposons d'apprendre les habitudes des utilisateurs afin de réduire la complexité de l’algorithme de reconnaissance d'activités. Pour se faire, notre méthode combine la reconnaissance d’activités et la prédiction des prochaines activités afin d’atteindre une consommation raisonnable des ressources du téléphone. Nous montrons que notre proposition réduit remarquablement la consommation de la batterie tout en gardant un taux de précision élevé

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