Juan Antonio Álvarez-García

Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 EvAAL competition

EvAAL is an annual international competition that addresses the “grand” challenge of evaluation and comparison of Ambient Assisted Living AAL systems and platforms, with the final goal to assess the autonomy, independent living and quality of life that AAL systems may grant to their end users. The 2012 Edition was focused on two pillars of AAL: Indoor localization and activity recognition. Results from both competitions suggest that there is still space for other editions not only to improve accuracy of such systems, but also their user acceptance and interoperability. This paper describes the organization and results of the 2012 edition.

Competitive Live Evaluations of Activity-Recognition Systems

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

Energy wasting at internet data centers due to fear

Data centers represent the hungriest consumers of energy within IT companies.Energy saving involves two direct benefits: sustainability and cost reduction.This paper provides measurement of risk aversion experienced by administrators. The fear experienced by datacenter administrators presents an ongoing problem due to the low percentage of machines that they are willing to switch off in order to save energy. This risk aversion can be assessed from a cognitive system. The purpose of this paper is to demonstrate the extra costs incurred by maintaining all the machines of a data center executing continuously for fear of damaging hardware, degradating the service, or losing data. To this end, an objective function which minimizes energy consumption depending on the number of times that the machines are switched on/off is provided. The risk aversion experienced by these data center administrators can be measured from the percentage of machines that they are willing to switch off. It is shown that it is always the best option to turn off machines in order to reduce costs, given a formulation of the cognitive aspects of the fear experienced by datacenter administrators.

Benchmarking real-time vehicle data streaming models for a smart city

Abstract The information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation service in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction.

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.

Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

Abstract This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB images applying a Deep Neural Network which comprises convolutional and spatial transformer layers. This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a Spanish traffic sign recognition dataset is released.

Detecting Social Interactions in Working Environments Through Sensing Technologies

The knowledge about social ties among humans is important to optimize several aspects concerning networking in mobile social networks. Generally, ties among people are detected on the base of proximity of people. We discuss here how ties concerning colleagues in an office can be detected by leveraging on a number of sociological markers like co-activity, proximity, speech activity and similarity of locations visited. We present the results from two data gathering campaigns located in Italy and Spain.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.

Evaluating Human Activity Recognition Systems for AAL Environments

EvAAL Activity Recognition track’s main goal is to evaluate one of the pillars of Ambient Assisted Living (AAL): human activity recognition (AR). In this edition 4 teams from United States, Ireland, Spain and Japan participated in the competition. Results show that accelerometer based solutions are promising due to their small size and their integration in complex devices such as mobile phones or elastics wearable straps.

Tracking system based on accelerometry for users with restricted physical activity

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. All this, is based on the data of accelerometry analysis which are obtained through a mobile phone. Besides this, we will develop a comprehensive system for consulting the activity obtained in order to provide families and care staff an interface through which to observe the condition of the individual subject to monitoring.