DeustoTeka Examinando por Autor "Azkune Galparsoro, Gorka"

Examinando por Autor "Azkune Galparsoro, Gorka"

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Combining users’ activity survey and simulators to evaluate human activity recognition systems
(MDPI AG, 2015-04-08) Azkune Galparsoro, Gorka; Almeida, Aitor; López de Ipiña González de Artaza, Diego; Chen, Liming
Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.
A comparative analysis of human behavior prediction approaches in intelligent environments
(MDPI, 2022-01-18) Almeida, Aitor; Bermejo Fernández, Unai ; Bilbao Jayo, Aritz ; Azkune Galparsoro, Gorka; Aguilera, Unai ; Emaldi, Mikel ; Dornaika, Fadi; Arganda-Carreras, Ignacio
Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling
Cross-environment activity recognition using word embeddings for sensor and activity representation
(Elsevier B.V., 2020-12-22) Azkune Galparsoro, Gorka; Almeida, Aitor; Agirre Bengoa, Eneko
Cross-environment activity recognition in smart homes is a very challenging problem, specially for data-driven approaches. Currently, systems developed to work for a certain environment degrade substantially when applied to a new environment, where not only sensors, but also the monitored activities may be different. Some systems require manual labeling and mapping of the new sensor names and activities using an ontology. Ideally, given a new smart home, we would like to be able to deploy the system, which has been trained on other sources, with minimal manual effort and with acceptable performance. In this paper, we propose the use of neural word embeddings to represent sensor activations and activities, which comes with several advantages: (i) the representation of the semantic information of sensor and activity names, and (ii) automatically mapping sensors and activities of different environments into the same semantic space. Based on this novel representation approach, we propose two data-driven activity recognition systems: the first one is a completely unsupervised system based on embedding similarities, while the second one adds a supervised learning regressor on top of them. We compare our approaches with some baselines using four public datasets, showing that data-driven cross-environment activity recognition obtains good results even when sensors and activity labels significantly differ. Our results show promise for reducing manual effort, and are complementary to other efforts using ontologies
Egocentric vision-based action recognition: a survey
(Elsevier B.V., 2022-02) Núñez Marcos, Adrián ; Azkune Galparsoro, Gorka ; Arganda-Carreras, Ignacio
The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.
Embedding-based real-time change point detection with application to activity segmentation in smart home time series data
(Elsevier Ltd, 2021-12-15) Bermejo Fernández, Unai; Almeida, Aitor; Bilbao Jayo, Aritz; Azkune Galparsoro, Gorka
Human activity recognition systems are essential to enable many assistive applications. Those systems can be sensor-based or vision-based. When sensor-based systems are deployed in real environments, they must segment sensor data streams on the fly in order to extract features and recognize the ongoing activities. This segmentation can be done with different approaches. One effective approach is to employ change point detection (CPD) algorithms to detect activity transitions (i.e. determine when activities start and end). In this paper, we present a novel real-time CPD method to perform activity segmentation, where neural embeddings (vectors of continuous numbers) are used to represent sensor events. Through empirical evaluation with 3 publicly available benchmark datasets, we conclude that our method is useful for segmenting sensor data, offering significant better performance than state of the art algorithms in two of them. Besides, we propose the use of retrofitting, a graph-based technique, to adjust the embeddings and introduce expert knowledge in the activity segmentation task, showing empirically that it can improve the performance of our method using three graphs generated from two sources of information. Finally, we discuss the advantages of our approach regarding computational cost, manual effort reduction (no need of hand-crafted features) and cross-environment possibilities (transfer learning) in comparison to others
Ezagutzan oinarritutako giza jardueren eredu dinamiko eta pertsonalizatuak ikasten
(Universidad del País Vasco = Euskal Herriko Unibertsitatea, Servicio de Publicaciones, 2016) Azkune Galparsoro, Gorka ; Almeida, Aitor; López de Ipiña González de Artaza, Diego; Chen, Liming
Gizakietara egokitutako teknologiak garatzeko, ezinbestekoa da makinek giza jarduerak antzemateko gaitasuna izatea, sentsoreak eta konputazio-gailuak erabiliz. Horretarako, antzeman nahi diren jarduera horien eredu konputazionalak sortu behar dira. Gaur egun, jarduera-ereduak sortzeko garaian, bi joera nagusi aurki daitezke: datuetan oinarritutako ereduak eta ezagutzan oinarritutakoak. Biek ere abantaila eta desabantailak dituzte. Lan honen helburua da bi joerak elkartzea eredu dinamiko eta pertsonalizatuak lortzeko, ezagutzan oinarritutako eredu orokor batzuetatik hasita. Modu horretan, pertsona bakoitzaren bilakaerara egokitutako modelatze-prozesuak lor daitezke.
An IoT-aware approach for elderly-friendly cities
(Institute of Electrical and Electronics Engineers Inc., 2018-03-12) Mulero, Rubén; Almeida, Aitor; Azkune Galparsoro, Gorka; Abril Jiménez, Patricia; Arredondo Waldmeyer, María Teresa; Páramo Castrillo, Miguel; Patrono, Luigi; Rametta, Piercosimo; Sergi, Ilaria
The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person.
Learning for dynamic and personalised knowledge-based activity models
(Universidad de Deusto, 2015-07-15) Azkune Galparsoro, Gorka; Chen, Liming; López de Ipiña González de Artaza, Diego; Facultad de Ingeniería; Ingeniería para la Sociedad de la Información y Desarrollo Sostenible
Human activity recognition is one of the key competences for human adaptive technologies. The idea of such technologies is to adapt their services to human users, so being able to recognise what human users are doing is an important step to adapt services suitably. One of the most promising approaches for human activity recognition is the knowledge-driven approach, which has already shown very interesting features and advantages. Knowledge-driven approaches allow using expert domain knowledge to describe activities and environments, providing efficient recognition systems. However, there are also some drawbacks, such as the usage of generic and static activity models, i.e. activities are defined by their generic features - they do not include personal specificities - and once activities have been defined, they do not evolve according to what users do. This dissertation presents an approach to using data-driven techniques to evolve knowledge-based activity models with a user¿s behavioural data. The approach includes a novel clustering process where initial incomplete models developed through knowledge engineering are used to detect action clusters which describe activities and aggregate new actions. Based on those action clusters, a learning process is then designed to learn and model varying ways of performing activities in order to acquire complete and specialised activity models. The approach has been tested with real users¿ inputs, noisy sensors and demanding activity sequences. Results have shown that the 100% of complete and specialised activity models are properly learnt at the expense of learning some false positive models.
Nola prestatzen duzu kafea?
(Elhuyar Fundazioa, 2017) Azkune Galparsoro, Gorka
Predicting human behaviour with recurrent neural networks
(MDPI AG, 2018-02-20) Almeida, Aitor; Azkune Galparsoro, Gorka
As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to mild cognitive impairment and frailty and on providing meaningful interventions that prevent these risks. As part of the risk detection process, we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, and intra- and inter-activity behaviour. Using this conceptual model, we have created a deep learning architecture based on long short-term memory networks (LSTMs) that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows us to predict the user's next actions and to identify anomalous user behaviours.
A scalable hybrid activity recognition: approach for intelligent environments
(Institute of Electrical and Electronics Engineers Inc., 2018-07-30) Azkune Galparsoro, Gorka; Almeida, Aitor
Human activity recognition is a key technology for ICT-based (infomation and communication technologies) assistive applications. The most successful activity recognition systems for intelligent environments in terms of performance rely on supervised learning techniques. However, those techniques demand large labelled data sets for specific sensor deployments and monitored person. Such requirements make supervised learning techniques not to scale well to real world deployments, where different sensor infrastructures may be used to monitor different users. In this paper, we present a novel activity recognition system, based on a combination of unsupervised learning techniques and knowledge-based activity models. First, we use a domain-specific data mining algorithm previously developed by Cook et al. to extract the most frequent action sequences executed by a person. Second, we insert knowledge-based activity models in a novel matching algorithm with the aim of inferring what activities are being performed in a given action sequence. The approach results on a scalable activity recognition system, which has been tested on three real data sets. The obtained performance is comparable to supervised learning techniques.
Smart cities survey: technologies, application domains and challenges for the cities of the future
(SAGE Publications Ltd, 2019-06-10) Sánchez Corcuera, Rubén ; Núñez Marcos, Adrián; Sesma Solance, Jesús; Bilbao Jayo, Aritz ; Mulero, Rubén; Zulaika Zurimendi, Unai ; Azkune Galparsoro, Gorka ; Almeida, Aitor
The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comfortable. The paradigm of Smart Cities arises as a response to the goal of creating the city of the future, where (1) the well-being and rights of their citizens are guaranteed, (2) industry and (3) urban planning is assessed from an environmental and sustainable viewpoint. Smart Cities still face some challenges in their implementation, but gradually more research projects of Smart Cities are funded and executed. Moreover, cities from all around the globe are implementing Smart City features to improve services or the quality of life of their citizens. Through this article, (1) we go through various definitions of Smart Cities in the literature, (2) we review the technologies and methodologies used nowadays, (3) we summarise the different domains of applications where these technologies and methodologies are applied (e.g. health and education), (4) we show the cities that have integrated the Smart City paradigm in their daily functioning and (5) we provide a review of the open research challenges. Finally, we discuss about the future opportunities for Smart Cities and the issues that must be tackled in order to move towards the cities of the future.
Vision-based fall detection with convolutional neural networks
(Hindawi Limited, 2017-12-06) Núñez Marcos, Adrián; Azkune Galparsoro, Gorka ; Arganda-Carreras, Ignacio
One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.
Zenbakien hizkuntza
(Elhuyar Fundazioa, 2018-11-30) Azkune Galparsoro, Gorka
Zientziak eta letrak. Zenbakiak eta hitzak. Bi mundu bereizi balira bezala aurkeztu izan zaizkigu ia beti. Elkarrekin dantza egin nahi ez duten izaki isolatu gisa. Hala eta guztiz ere, gure garunek badakite bai zenbakiak bai hitzak behar bezala lotzen. Zenbakiak eta hitzak darabiltzagu unibertsoa ulertzeko, haren misterioak bizitzeko, gizakion sentimendu eta nahiak adierazteko. Agian, hor, haien sorreran, topatu beharko genuke haien arteko lotura: adimenean.

Examinando por Autor "Azkune Galparsoro, Gorka"

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