Detección de actividades en tiempo real con sensores multimodales en una Smart Home

RESUMEN: A medida que pasan los años, la esperanza de vida va aumentando, creciendo también el número de personas mayores con necesidades de atención que viven solas. De esta forma, las tecnologías proporcionan una herramienta para permitir la independencia de estas personas de forma segura. Así, mediante el reconocimiento de actividades, se permite la monitorización de los usuarios en la vivienda, detectando posibles anomalías y cambios en el comportamiento. En este proyecto, se propone un sistema de reconocimiento de actividades en tiempo real de los usuarios de una vivienda. Este sistema está formado por dispositivos como sensores binarios, de localización y smartwatches. Los datos que proporcionan estos, se incluyen en un modelo de machine learning para obtener las actividades que realizan los usuarios en la vivienda. ABSTRACT: As the years go by, life expectancy is increasing, and the number of older people with care needs living alone is also growing. In this way, technologies provide a tool to enable these people to be independent in a safe manner. Thus, by means of activity recognition, it is possible to monitor users in the home, detecting possible anomalies and changes in behavior. In this project, a real-time activity recognition system of the users of a house is proposed. This system is made up of devices such as binary and location sensors and smartwatches. The data provided by these are included in a machine learning model to obtain the activities carried out by the users in the house

DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition

Accelerating neural network architecture search using multi-GPU high-performance computing

Neural networks stand out from artificial intelligence because they can complete challenging tasks, such as image classification. However, designing a neural network for a particular problem requires experience and tedious trial and error. Automating this process defines a research field usually relying on population-based meta-heuristics. This kind of optimizer generally needs numerous function evaluations, which are computationally demanding in this context as they involve building, training, and evaluating different neural networks. Fortunately, these algorithms are also well suited for parallel computing. This work describes how the teaching–learning-based optimization algorithm has been adapted for designing neural networks exploiting a multi-GPU high-performance computing environment. The optimizer, not applied before for this purpose up to the authors’ knowledge, has been selected because it lacks specific parameters and is compatible with large-scale optimization. Thus, its configuration does not result in another problem and could design architectures with many layers. The parallelization scheme is decoupled from the optimizer. It can be seen as an external evaluation service managing multiple GPUs for promising neural network designs, even at different machines, and multiple CPU’s for low-performing solutions. This strategy has been tested in designing a neural network for image classification based on the CIFAR-10 dataset. The architectures found outperform human designs, and the sequential process is accelerated 4.2 times with 4 GPUs and 96 cores thanks to parallelization, being the ideal speed up 4.39 in this case